50 results on '"Christophe Lenglet"'
Search Results
2. Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain
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Mark D. Grier, Essa Yacoub, Gregor Adriany, Russell L. Lagore, Noam Harel, Ru-Yuan Zhang, Christophe Lenglet, Kâmil Uğurbil, Jan Zimmermann, and Sarah R. Heilbronner
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Diffusion MRI ,Structural connectivity ,Nonhuman primate ,Tractography ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields. more...
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- 2022
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3. NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing
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Steen Moeller, Pramod Kumar Pisharady, Sudhir Ramanna, Christophe Lenglet, Xiaoping Wu, Logan Dowdle, Essa Yacoub, Kamil Uğurbil, and Mehmet Akçakaya
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Denoising ,Singular value decomposition ,Simultaneous multi-slice ,Multiband ,Diffusion MRI ,Human connectome project ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with DIstribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method. more...
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- 2021
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4. Robustness of Brain Structural Networks Is Affected in Cognitively Impaired MS Patients
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Hamza Farooq, Christophe Lenglet, and Flavia Nelson
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cognitive impairment ,multiple sclerosis ,diffusion MRI ,brain networks ,imaging bio-markers ,Ollivier-Ricci curvature ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
The robustness of brain structural networks, estimated from diffusion MRI data, may be relevant to cognition. We investigate whether measures of network robustness, such as Ollivier-Ricci curvature, can explain cognitive impairment in multiple sclerosis (MS). We assessed whether local (i.e., cortical area) and/or global (i.e., whole brain) robustness, differs between cognitively impaired (MSCI) and non-impaired (MSNI) MS patients. Fifty patients, with Expanded Disability Status Scale mean (m): 3.2, disease duration m: 12 years, and age m: 40 years, were enrolled. Cognitive impairment scores were estimated from the Minimal Assessment of Cognitive Function in Multiple Sclerosis. Images were obtained in a 3T MRI using a diffusion protocol with a 2 min acquisition time. Brain structural networks were created using 333 cortical areas. Local and global robustness was estimated for each individual, and comparisons were performed between MSCI and MSNI patients. 31 MSCI and 10 MSNI patients were included in the analyses. Brain structural network robustness and centrality showed significant correlations with cognitive impairment. Measures of network robustness and centrality identified specific cortical areas relevant to MS-related cognitive impairment. These measures can be obtained on clinical scanners and are succinct yet accurate potential biomarkers of cognitive impairment. more...
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- 2020
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5. Assessment of Cerebral and Cerebellar White Matter Microstructure in Spinocerebellar Ataxias 1, 2, 3, and 6 Using Diffusion MRI
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Young Woo Park, James M. Joers, Bin Guo, Diane Hutter, Khalaf Bushara, Isaac M. Adanyeguh, Lynn E. Eberly, Gülin Öz, and Christophe Lenglet
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SCA1 ,SCA2 ,SCA3 ,SCA6 ,diffusion MRI ,Spinocerebeflar ataxias ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Development of imaging biomarkers for rare neurodegenerative diseases such as spinocerebellar ataxia (SCA) is important to non-invasively track progression of disease pathology and monitor response to interventions. Diffusion MRI (dMRI) has been shown to identify cross-sectional degeneration of white matter (WM) microstructure and connectivity between healthy controls and patients with SCAs, using various analysis methods. In this paper, we present dMRI data in SCAs type 1, 2, 3, and 6 and matched controls, including longitudinal acquisitions at 12–24-month intervals in a subset of the cohort, with up to 5 visits. The SCA1 cohort also contained 3 premanifest patients at baseline, with 2 showing ataxia symptoms at the time of the follow-up scans. We focused on two aspects: first, multimodal evaluation of the dMRI data in a cross-sectional approach, and second, longitudinal trends in dMRI data in SCAs. Three different pipelines were used to perform cross-sectional analyses in WM: region of interest (ROI), tract-based spatial statistics (TBSS), and fixel-based analysis (FBA). We further analyzed longitudinal changes in dMRI metrics throughout the brain using ROI-based analysis. Both ROI and TBSS analyses identified higher mean (MD), axial (AD), and radial (RD) diffusivity and lower fractional anisotropy (FA) in the cerebellum for all SCAs compared to controls, as well as some cerebral alterations in SCA1, 2, and 3. FBA showed lower fiber density (FD) and fiber crossing (FC) regions similar to those identified by ROI and TBSS analyses. FBA also highlighted corticospinal tract (CST) abnormalities, which was not detected by the other two pipelines. Longitudinal ROI-based analysis showed significant increase in AD in the middle cerebellar peduncle (MCP) for patients with SCA1, suggesting that the MCP may be a good candidate region to monitor disease progression. The patient who remained symptom-free throughout the study displayed no microstructural abnormalities. On the other hand, the two patients who were at the premanifest stage at baseline, and showed ataxia symptoms in their follow-up visits, displayed AD values in the MCP that were already in the range of symptomatic patients with SCA1 at their baseline visit, demonstrating that microstructural abnormalities are detectable prior to the onset of ataxia. more...
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- 2020
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6. Diffusion magnetic resonance imaging reveals tract‐specific microstructural correlates of electrophysiological impairments in non‐myelopathic and myelopathic spinal cord compression
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René Labounek, Tomáš Rohan, Josef Bednařík, Zdeněk Kadaňka, Miloš Keřkovský, Eva Vlčková, Magda Horáková, Alena Svátková, Petr Hluštík, Christophe Lenglet, Jan Valošek, Petr Bednařík, Julien Cohen-Adad, Jan Kočica, and Tomáš Horák more...
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Pathology ,medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,Magnetic resonance imaging ,Sensory system ,Electromyography ,medicine.disease ,Asymptomatic ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Electrophysiology ,Myelopathy ,0302 clinical medicine ,Neurology ,Spinal cord compression ,medicine ,Neurology (clinical) ,medicine.symptom ,business ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
BACKGROUND AND PURPOSE Non-myelopathic degenerative cervical spinal cord compression (NMDC) frequently occurs throughout aging and may progress to potentially irreversible degenerative cervical myelopathy (DCM). Whereas standard clinical magnetic resonance imaging (MRI) and electrophysiological measures assess compression severity and neurological dysfunction, respectively, underlying microstructural deficits still have to be established in NMDC and DCM patients. The study aims to establish tract-specific diffusion MRI markers of electrophysiological deficits to predict the progression of asymptomatic NMDC to symptomatic DCM. METHODS High-resolution 3 T diffusion MRI was acquired for 103 NMDC and 21 DCM patients compared to 60 healthy controls to reveal diffusion alterations and relationships between tract-specific diffusion metrics and corresponding electrophysiological measures and compression severity. Relationship between the degree of DCM disability, assessed by the modified Japanese Orthopaedic Association scale, and tract-specific microstructural changes in DCM patients was also explored. RESULTS The study identified diffusion-derived abnormalities in the gray matter, dorsal and lateral tracts congruent with trans-synaptic degeneration and demyelination in chronic degenerative spinal cord compression with more profound alterations in DCM than NMDC. Diffusion metrics were affected in the C3-6 area as well as above the compression level at C3 with more profound rostral deficits in DCM than NMDC. Alterations in lateral motor and dorsal sensory tracts correlated with motor and sensory evoked potentials, respectively, whereas electromyography outcomes corresponded with gray matter microstructure. DCM disability corresponded with microstructure alteration in lateral columns. CONCLUSIONS Outcomes imply the necessity of high-resolution tract-specific diffusion MRI for monitoring degenerative spinal pathology in longitudinal studies. more...
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- 2021
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7. HARDI-ZOOMit protocol improves specificity to microstructural changes in presymptomatic myelopathy
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Petr Bednařík, Julien Cohen-Adad, Magda Horáková, René Labounek, Tomáš Horák, Petr Hluštík, Jan Valošek, Josef Bednařík, Igor Nestrasil, Alena Svátková, Christophe Lenglet, and Lubomír Vojtíšek
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Adult ,Male ,Biomedical Engineering ,lcsh:Medicine ,Spinal cord diseases ,Anterior white commissure ,Signal-To-Noise Ratio ,computer.software_genre ,Sensitivity and Specificity ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Myelopathy ,0302 clinical medicine ,Voxel ,medicine ,Cluster Analysis ,Humans ,Spine structure ,lcsh:Science ,Reproducibility ,Multidisciplinary ,medicine.diagnostic_test ,business.industry ,lcsh:R ,Data acquisition ,Reproducibility of Results ,Magnetic resonance imaging ,Diagnostic markers ,Cervical cord compression ,Middle Aged ,medicine.disease ,Spinal cord ,3. Good health ,medicine.anatomical_structure ,Diffusion Magnetic Resonance Imaging ,Diffusion Tensor Imaging ,Case-Control Studies ,Cervical Vertebrae ,lcsh:Q ,Female ,Nuclear medicine ,business ,computer ,Spinal Cord Compression ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Diffusion magnetic resonance imaging (dMRI) proved promising in patients with non-myelopathic degenerative cervical cord compression (NMDCCC), i.e., without clinically manifested myelopathy. Aim of the study is to present a fast multi-shell HARDI-ZOOMit dMRI protocol and validate its usability to detect microstructural myelopathy in NMDCCC patients. In 7 young healthy volunteers, 13 age-comparable healthy controls, 18 patients with mild NMDCCC and 15 patients with severe NMDCCC, the protocol provided higher signal-to-noise ratio, enhanced visualization of white/gray matter structures in microstructural maps, improved dMRI metric reproducibility, preserved sensitivity (SE = 87.88%) and increased specificity (SP = 92.31%) of control-patient group differences when compared to DTI-RESOLVE protocol (SE = 87.88%, SP = 76.92%). Of the 56 tested microstructural parameters, HARDI-ZOOMit yielded significant patient-control differences in 19 parameters, whereas in DTI-RESOLVE data, differences were observed in 10 parameters, with mostly lower robustness. Novel marker the white-gray matter diffusivity gradient demonstrated the highest separation. HARDI-ZOOMit protocol detected larger number of crossing fibers (5–15% of voxels) with physiologically plausible orientations than DTI-RESOLVE protocol (0–8% of voxels). Crossings were detected in areas of dorsal horns and anterior white commissure. HARDI-ZOOMit protocol proved to be a sensitive and practical tool for clinical quantitative spinal cord imaging. more...
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- 2020
8. Cortical fibers orientation mapping using in-vivo whole brain 7 T diffusion MRI
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An T. Vu, Omer Faruk Gulban, Essa Yacoub, Christophe Lenglet, Kamil Ugurbil, Federico De Martino, Audition, and RS: FPN CN 2
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Adult ,Materials science ,Image Processing, Computer-Assisted/methods ,Anisotropic diffusion ,Cognitive Neuroscience ,Image Processing ,Neuroimaging ,Mri studies ,Article ,030218 nuclear medicine & medical imaging ,White matter ,03 medical and health sciences ,0302 clinical medicine ,Nerve Fibers ,medicine ,High spatial resolution ,Image Processing, Computer-Assisted ,Computer-Assisted/methods ,Neurites ,Humans ,Neuroimaging/methods ,Cerebral Cortex ,Depth dependent ,Cerebral Cortex/anatomy & histology ,Dendrites ,Axons ,Diffusion Magnetic Resonance Imaging/methods ,Bias effect ,medicine.anatomical_structure ,Diffusion Magnetic Resonance Imaging ,Neurology ,Diffusion gradient ,030217 neurology & neurosurgery ,Diffusion MRI ,Biomedical engineering - Abstract
Diffusion MRI of the cortical gray matter is challenging because the micro-environment probed by water molecules is much more complex than within the white matter. High spatial and angular resolutions are therefore necessary to uncover anisotropic diffusion patterns and laminar structures, which provide complementary (e.g. to anatomical and functional MRI) microstructural information about the cortex architectonic. Several ex-vivo and in-vivo MRI studies have recently addressed this question, however predominantly with an emphasis on specific cortical areas. There is currently no whole brain in-vivo data leveraging multi-shell diffusion MRI acquisition at high spatial resolution, and depth dependent analysis, to characterize the complex organization of cortical fibers. Here, we present unique in-vivo human 7T diffusion MRI data, and a dedicated cortical depth dependent analysis pipeline. We leverage the high spatial (1.05 mm isotropic) and angular (198 diffusion gradient directions) resolution of this whole brain dataset to improve cortical fiber orientations mapping, and study neurites (axons and/or dendrites) trajectories across cortical depths. Tangential fibers in superficial cortical depths and crossing fiber configurations in deep cortical depths are identified. Fibers gradually inserting into the gyral walls are visualized, which contributes to mitigating the gyral bias effect. Quantitative radiality maps and histograms in individual subjects and cortex-based aligned datasets further support our results. more...
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- 2018
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9. High‐resolution whole‐brain diffusion MRI at 7T using radiofrequency parallel transmission
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Sebastian Schmitter, Xiaoping Wu, Christophe Lenglet, Pierre-Francois Van de Moortele, Kâmil Uğurbil, Edward J. Auerbach, An T. Vu, Essa Yacoub, and Steen Moeller
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Adult ,Male ,Materials science ,Adolescent ,High resolution ,Signal ,Article ,030218 nuclear medicine & medical imaging ,Scan time ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Connectome ,Image Processing, Computer-Assisted ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Aged ,Human Connectome Project ,Power deposition ,Brain ,Middle Aged ,Sagittal plane ,Diffusion Magnetic Resonance Imaging ,medicine.anatomical_structure ,Parallel communication ,Female ,Algorithms ,030217 neurology & neurosurgery ,Biomedical engineering ,Diffusion MRI - Abstract
PURPOSE: Investigating the utility of RF parallel transmission (pTx) for Human Connectome Project (HCP)-style whole-brain diffusion MRI (dMRI) data at 7 Tesla (7T). METHODS: Healthy subjects were scanned in pTx and single-transmit (1Tx) modes. Multiband (MB), single-spoke pTx pulses were designed to image sagittal slices. HCP-style dMRI data (i.e., 1.05-mm resolutions, MB2, b-values=1000/2000 s/mm(2), 286 images and 40-minute scan) and data with higher accelerations (MB3 and MB4) were acquired with pTx. RESULTS: pTx significantly improved flip-angle detected signal uniformity across the brain, yielding ~19% increase in temporal signal-to-noise ratio (tSNR) averaged over the brain relative to 1Tx. This allowed significantly enhanced estimation of multiple fiber orientations (with ~21% decrease in dispersion) in HCP-style 7T dMRI datasets. Additionally, pTx pulses achieved substantially lower power deposition, permitting higher accelerations, enabling collection of the same data in 2/3 and 1/2 the scan time or of more data in the same scan time. CONCLUSION: pTx provides a solution to two major limitations for slice-accelerated high-resolution whole-brain dMRI at 7T; it improves flip-angle uniformity, and enables higher slice acceleration relative to current state-of-the-art. As such, pTx provides significant advantages for rapid acquisition of high-quality, high-resolution truly whole brain dMRI data. more...
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- 2018
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10. Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning
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Christophe Lenglet, Pramod Kumar Pisharady, Stamatios N. Sotiropoulos, Julio M. Duarte-Carvajalino, and Guillermo Sapiro
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Computer science ,Cognitive Neuroscience ,Neuroimaging ,Sparse signal recovery ,Bayesian inference ,Nerve Fibers, Myelinated ,Article ,Diffusion MRI ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Image Interpretation, Computer-Assisted ,Prior probability ,Humans ,Linear unmixing ,Hyperparameter ,K-SVD ,business.industry ,Estimation theory ,Fiber orientation ,Bayes Theorem ,Pattern recognition ,Sparse approximation ,Compressive sensing ,Models, Theoretical ,White Matter ,Diffusion Magnetic Resonance Imaging ,Compressed sensing ,Neurology ,Sparse Bayesian learning ,Artificial intelligence ,Deconvolution ,business ,Algorithms ,030217 neurology & neurosurgery - Abstract
We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates. more...
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- 2018
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11. Advances in computational and statistical diffusion MRI
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Lauren J. O'Donnell, Alessandro Daducci, Christophe Lenglet, Demian Wassermann, Laboratory of Mathematics in Imaging [Boston], Brigham and Women's Hospital [Boston], Laboratoire de Traitement du signal [EPFL] / Signal Processing Laboratories (SP Lab), Ecole Polytechnique Fédérale de Lausanne (EPFL), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) more...
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Registration ,Computer science ,[PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph] ,Population ,Statistics as Topic ,Context (language use) ,Machine learning ,computer.software_genre ,Article ,030218 nuclear medicine & medical imaging ,Diffusion MRI ,03 medical and health sciences ,0302 clinical medicine ,medicine ,diffusion MRI ,registration ,statistics ,tractography ,Image Processing, Computer-Assisted ,Humans ,Radiology, Nuclear Medicine and imaging ,Computer Simulation ,Diffusion (business) ,education ,Spectroscopy ,Computer memory ,CIBM-AIT ,Processor time ,education.field_of_study ,medicine.diagnostic_test ,business.industry ,Magnetic resonance imaging ,White Matter ,Diffusion Magnetic Resonance Imaging ,Statictics ,Molecular Medicine ,Artificial intelligence ,business ,computer ,Tractography ,030217 neurology & neurosurgery - Abstract
International audience; Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole‐brain connectivity information that describes the brain's wiring diagram and population‐based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high‐level overview of interest to diffusion MRI researchers, with a more in‐depth treatment to illustrate selected computational advances. more...
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- 2019
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12. Intraspinal space restriction at the occipito-cervical junction alters cervical spinal cord diffusion MRI metrics in mucopolysacharidoses patients
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René Labounek, Christophe Lenglet, Carol Nguyen, Julien Cohen-Adad, Ivan Krasovec, Igor Nestrasil, Chester B. Whitley, Jan Valošek, and Alena Svátková
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Endocrinology ,medicine.anatomical_structure ,business.industry ,Endocrinology, Diabetes and Metabolism ,Genetics ,medicine ,Anatomy ,Spinal cord ,Space (mathematics) ,business ,Molecular Biology ,Biochemistry ,Diffusion MRI - Published
- 2020
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13. Classifying Adolescent Major Depressive Disorder using Linear SVM with Anatomical Features from Diffusion Weighted Imaging
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Keshab K. Parhi, Kathryn R. Cullen, Mindy Westlund Schreiner, Shu-Hsien Chu, Christophe Lenglet, and Bonnie Klimes-Dougan
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Multivariate statistics ,business.industry ,Linear svm ,Univariate ,Pattern recognition ,medicine.disease ,behavioral disciplines and activities ,030227 psychiatry ,03 medical and health sciences ,0302 clinical medicine ,Betweenness centrality ,mental disorders ,Medicine ,Major depressive disorder ,Right lingual gyrus ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,Statistical hypothesis testing ,Diffusion MRI - Abstract
Adolescence is a period of rapid brain maturation and a critical period for the onset of Major Depressive Disorder (MDD) that usually leads to serious outcomes such as suicide. Although changes in anatomical connectivity in MDD have been reported, changes in network topology for MDD remain unclear. Additionally, whether the changes are the same for adolescent MDD and adult MDD remains unclear as well. This paper explores anatomical features including: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks, and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. In addition to statistical tests, univariate classifiers are designed to evaluate the discriminating power of features. Furthermore, multivariate classifiers are trained for distinguishing healthy subjects from MDD patients. The best classifier achieves an accuracy of 76.56%, 81.08% sensitivity, 70.37% specificity and 78.95% precision for 64 subjects (37 MDD and 27 matched healthy control). The selected features include: 1) betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, 2) participation coefficient of the right pars opercularis of the AD network at 16% sparsity, 3) participation coefficient of the left insular cortex of the MD network at 21% sparsity, and 4) participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. These features reflect changes in the topological structure of the brain anatomical network in MDD. more...
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- 2018
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14. Biomarkers for Adolescent MDD from Anatomical Connectivity and Network Topology Using Diffusion MRI
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Bonnie Klimes-Dougan, Keshab K. Parhi, Kathryn R. Cullen, Mindy Westlund Schreiner, Shu-Hsien Chu, and Christophe Lenglet
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medicine.medical_specialty ,Adolescent ,Hippocampus ,Lateralization of brain function ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Text mining ,Internal medicine ,Cortex (anatomy) ,medicine ,Humans ,Depressive Disorder, Major ,business.industry ,Case-control study ,Brain ,medicine.disease ,030227 psychiatry ,Diffusion Magnetic Resonance Imaging ,Bonferroni correction ,medicine.anatomical_structure ,Case-Control Studies ,symbols ,Cardiology ,Major depressive disorder ,business ,Biomarkers ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Due to the high resistance (35%) to the current treatment methods in adolescent Major Depressive Disorder (MDD) and its tragic outcomes, the discovery of treatmentrelated responders is critical to developing effective treatments. In this paper, the permutation test is performed to identify statistically significant changes in anatomical characteristics during pairwise comparisons among the control group (n=27), treated MDD group (n=37), and untreated MDD group (n=15). The anatomical characteristics include: 1) anatomical connectivity defined using DTI metrics between a pair of brain regions, and 2) topological measurements of anatomical networks. With the Bonferroni correction for multiple-comparison, significant alterations in community structure and local topology were identified as the p-value < 5%, which include: 1) a reduced nodal centrality (degree and strength) on right hippocampus for treated compared to untreated group, 2) an elevated clustering coefficient and local efficiency on right lateral orbitofrontal cortex for untreated compared to the combination of control and treated groups, 3) an increased participation coefficient for untreated patients on left insula cortex in the meandiffusivity network compared to the combination of control and treated groups, and 4) a degraded module degree z-score on right caudate nucleus for all the patients compared to the control group. Two connections, hippocampus-insula in the right hemisphere and parahippocampal-insula in the left hemisphere, were found significantly altered in TR, AD, and FA due to MDD. more...
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- 2018
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15. Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier
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Bonnie Klimes-Dougan, Kathryn R. Cullen, Keshab K. Parhi, Mindy Westlund Schreiner, Christophe Lenglet, and Shu-Hsien Chu
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Support Vector Machine ,Adolescent ,Computer science ,Feature extraction ,03 medical and health sciences ,Svm classifier ,0302 clinical medicine ,Text mining ,Betweenness centrality ,medicine ,Humans ,Depressive Disorder, Major ,business.industry ,Brain ,Pattern recognition ,Mental illness ,medicine.disease ,030227 psychiatry ,Support vector machine ,Diffusion Tensor Imaging ,Major depressive disorder ,Artificial intelligence ,business ,Biomarkers ,030217 neurology & neurosurgery ,Pars opercularis ,Diffusion MRI - Abstract
Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. The features of interest include metrics in two categories: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks. A combination of p-value based filtering and minimum redundancy maximum relevance method is performed to select features for optimal classification accuracy. A leave-one-out cross-validation method is used for the classification performance evaluation. The proposed methodology achieves an improved accuracy of 78%, 90.39% sensitivity, and 79.66% precision for 79 subjects. The most distinguishing features are the betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, the participation coefficient of the right lateral occipital sulcus of the ADC network at 22% sparsity, the participation coefficient of the right pars opercularis of the AD network at 16% sparsity, and the participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. Those network measures reflect the change of connectivity between the regions and their associated anatomical subnetworks. more...
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- 2018
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16. Classifying Treated vs. Untreated MDD Adolescents from Anatomical Connectivity using Nonlinear SVM
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Christophe Lenglet, Shu-Hsien Chu, Mindy Westlund Schreiner, Kathryn R. Cullen, Keshab K. Parhi, and Bonnie Klimes-Dougan
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Male ,Support Vector Machine ,Adolescent ,Hippocampus ,Feature selection ,Corpus callosum ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Text mining ,Betweenness centrality ,medicine ,Humans ,Effective diffusion coefficient ,Anterior cingulate cortex ,Mathematics ,Depressive Disorder, Major ,business.industry ,Brain ,Pattern recognition ,030227 psychiatry ,Diffusion Tensor Imaging ,medicine.anatomical_structure ,Case-Control Studies ,Female ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Identification of the treatment-related responders for adolescent Major Depressive Disorder (MDD) is urgently needed to develop effective treatments. In this paper, machine learning based classifiers are used to reveal anatomical features as responders for distinguishing MDD patients who have received treatment from those who never received any treatment. The features are drawn from two sets of measurements: 1) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and 2) topological measurements from anatomical networks. Feature selection was performed based on p-value and minimum redundancy maximum relevance (mRMR) method to achieve improved classification accuracy. The classification performance is evaluated with a leave-one-out cross-validation method using 37 treated and 15 untreated subjects. The proposed methodology achieves 73% accuracy, 100% specificity, and 100% precision for 52 subjects. The most distinguishing features are the strength of the right hippocampus of the mean diffusivity (MD) network at 18% density and of the track-count (TR) network, the participation coefficient of the left middle temporal gyrus of the radial diffusivity (RD) network at 20% density, the axial diffusivity (AD) connectivity between right middle temporal gyrus and right supramarginal gyrus, the betweenness centrality of the right hippocampus of the TR network at 11% density, the apparent diffusion coefficient (ADC) connectivity between the left pars opercularis and the left rostral anterior cingulate cortex, the clustering coefficient of the middle anterior corpus callosum of the TR network at 11% density, and the AD connectivity between the left pars opercularis and the left rostral anterior cingulate cortex. more...
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- 2018
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17. Fast In Vivo High-Resolution Diffusion MRI of the Human Cervical Spinal Cord Microstructure
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Zuzana Piskořová, René Labounek, Josef Bednařík, Jakub Zimolka, Pavel Hok, Petr Bednařík, Jan Valošek, Petr Hluštík, Christophe Lenglet, Lubomír Vojtíšek, Alena Svátková, and Tomáš Horák
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Scanner ,Materials science ,medicine.diagnostic_test ,Resolution (electron density) ,Magnetic resonance imaging ,030218 nuclear medicine & medical imaging ,White matter ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Fractional anisotropy ,medicine ,Image resolution ,030217 neurology & neurosurgery ,Diffusion MRI ,Biomedical engineering ,Interpolation - Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is a widely-utilized method for assessment of microstructural properties in the central nervous system i.e., the brain and spinal cord (SC). In the SC, almost all previous human studies utilized Diffusion Tensor Imaging (DTI), which cannot accurately model areas where white matter (WM) pathways cross or diverge. While High Angular Diffusion Resolution Imaging (HARDI) can overcome some of these limitations, longer acquisition times critically limit its applicability to clinical human studies. In addition, previous human HARDI studies have used limited spatial resolution, with typically a few slices and voxel size ~1 × 1 × 5 mm3 being acquired in tens of minutes. Thus, we have optimized a novel fast HARDI protocol that allows collecting dMRI data at high angular and spatial resolutions in clinically-feasible time. Our data was acquired, using a 3T Siemens Prisma scanner, in less than 9 min. It has a total of 75 diffusion-weighted volumes and high spatial resolution of 0.67 × 0.67 × 3 mm3 (after interpolation in Fourier space) covering the cervical segments C4–C6. Our preliminary results demonstrate applicability of our technique in healthy individuals with good correspondence between low fractional anisotropy (FA) gray matter areas from the dMRI scans, and the same regions delineated on T2-weighted MR images with spatial resolution of 0.35 × 0.35 × 2.5 mm3. Our data also allows the detection of crossing fibers that were previously shown in vivo only in animal studies. more...
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- 2018
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18. Brain Parcellation and Connectivity Mapping Using Wasserstein Geometry
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Yongxin Chen, Tryphon T. Georgiou, Christophe Lenglet, and Hamza Farooq
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Human Connectome Project ,Computer science ,Voxel ,Wasserstein metric ,Metric (mathematics) ,Probabilistic logic ,Probability distribution ,Geometry ,computer.software_genre ,computer ,Diffusion MRI ,Tractography - Abstract
Several studies have used structural connectivity information to parcellate brain areas like the corpus callosum, thalamus, substantia nigra or motor cortex, which is otherwise difficult to achieve using conventional MRI techniques. They typically employ diffusion MRI (dMRI) tractography and compare connectivity profiles from individual voxels using correlation. However, this is potentially limiting since the profile signals (e.g. probabilistic connectivity maps) have non-zero values only in restricted areas of the brain, and correlation coefficients do not fully capture differences between connectivity profiles . Our first contribution is to introduce the Wasserstein distance as a metric to compare connectivity profiles, viewed as distributions. The Wasserstein metric (also known as Optimal Mass Transport cost or, Earth Mover’s distance) is natural as it allows a global comparison between probability distributions. Thereby, it relies not only on non-zero values but also takes into account their spatial pattern, which is crucial for the comparison of the brain connectivity profiles. Once a brain area is parcellated into anatomically relevant sub-regions, it is of interest to determine how voxels within each sub-region are collectively connected to the rest of the brain. The commonly used arithmetic mean of connectivity profiles fails to account for anatomical features and can easily over-emphasize spurious pathways. Therefore, our second contribution is to introduce the concept of Wasserstein barycenters of distributions, to estimate “average” connectivity profiles, and assess whether these are more representative of the neuroanatomy. We demonstrate the benefits of using the Wasserstein geometry to parcellate and “average” probabilistic tractography results from a realistic phantom dataset, as well as in vivo data from the Human Connectome Project. more...
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- 2018
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19. High resolution whole brain diffusion imaging at 7 T for the Human Connectome Project
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Essa Yacoub, Edward J. Auerbach, Stamatios N. Sotiropoulos, Jesper L. R. Andersson, Christophe Lenglet, Steen Moeller, An T. Vu, Saâd Jbabdi, and Kamil Ugurbil
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Signal processing ,Human Connectome Project ,Computer science ,business.industry ,Cognitive Neuroscience ,Brain ,Signal Processing, Computer-Assisted ,Image processing ,Pattern recognition ,Signal-To-Noise Ratio ,Article ,Temporal lobe ,Diffusion Magnetic Resonance Imaging ,Neurology ,Connectome ,Image Processing, Computer-Assisted ,Humans ,Artificial intelligence ,Artifacts ,business ,Simulation ,Diffusion MRI - Abstract
Mapping structural connectivity in healthy adults for the Human Connectome Project (HCP) benefits from high quality, high resolution, multiband (MB)-accelerated whole brain diffusion MRI (dMRI). Acquiring such data at ultrahigh fields (7 T and above) can improve intrinsic signal-to-noise ratio (SNR), but suffers from shorter T2 and T2* relaxation times, increased B1+ inhomogeneity (resulting in signal loss in cerebellar and temporal lobe regions), and increased power deposition (i.e. Specific Absorption Rate (SAR)), thereby limiting our ability to reduce the repetition time (TR). Here, we present recent developments and optimizations in 7 T image acquisitions for the HCP that allow us to efficiently obtain high-quality, high-resolution whole brain in-vivo dMRI data at 7 T. These data show spatial details typically seen only in ex-vivo studies and complement already very high quality 3 T HCP data in the same subjects. The advances are the result of intensive pilot studies aimed at mitigating the limitations of dMRI at 7 T. The data quality and methods described here are representative of the datasets that will be made freely available to the community in 2015. more...
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- 2015
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20. Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data
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Bennett A. Landman, Steen Moeller, Ian J. Deary, Thomas E. Nichols, Jessika E. Sussmann, David C. Glahn, Joanna M. Wardlaw, Rene L. Olvera, Stamatios N. Sotiropoulos, Susan N. Wright, David C. Van Essen, Rachel M. Brouwer, Binish Patel, John M. Starr, Dennis van 't Ent, Douglas E. Williamson, Christophe Lenglet, Nicholas G. Martin, Laura Almasy, Charles P. Peterson, Anouk den Braber, Saad Jbabdi, Katie L. McMahon, Peter Kochunov, Margie Wright, John Blangero, Braxton D. Mitchell, Hilleke E. Hulshoff Pol, Edward J. Auerbach, Jesper L. R. Andersson, Paul M. Thompson, Eco J. C. de Geus, Andrew M. McIntosh, Daniel S. Marcus, Stuart J. Ritchie, Ahmad R. Hariri, Greig I. deZubicaray, Emma Sprooten, Timothy E.J. Behrens, Joanne E. Curran, Peter T. Fox, Neda Jahanshad, Essa Yacoub, Dorret I. Boomsma, Mark E. Bastin, Kimm J. E. van Hulzen, Anderson M. Winkler, Marcel P. Zwiers, Kamil Ugurbil, L. Elliot Hong, René S. Kahn, Ravindranath Duggirala, Herve Lemaitre, Biological Psychology, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, Neuroscience Campus Amsterdam - Brain Imaging Technology, Neurology, NCA - Neurobiology of mental health, and NCA - Brain imaging technology more...
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Adult ,Male ,Netherlands Twin Register (NTR) ,Cognitive Neuroscience ,Twin Study ,Research Support ,Article ,N.I.H ,Cohort Studies ,Young Adult ,Research Support, N.I.H., Extramural ,Fractional anisotropy ,Connectome ,Journal Article ,Humans ,Comparative Study ,Genetic variability ,Registries ,Non-U.S. Gov't ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,Human Connectome Project ,Research Support, Non-U.S. Gov't ,Extramural ,Heritability ,Twin study ,White Matter ,Diffusion Tensor Imaging ,Neurology ,Evolutionary biology ,Nerve tract ,Anisotropy ,Female ,Genetic Phenomena ,Nerve Net ,Psychology ,Neuroscience ,Diffusion MRI - Abstract
Item does not contain fulltext The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p more...
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- 2015
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21. A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI
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Guillermo Sapiro, Stamatios N. Sotiropoulos, Pramod Kumar Pisharady, and Christophe Lenglet
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Computer science ,computer.software_genre ,Bayesian inference ,Article ,030218 nuclear medicine & medical imaging ,White matter ,03 medical and health sciences ,0302 clinical medicine ,Voxel ,medicine ,Gamma distribution ,Humans ,Hyperparameter ,Human Connectome Project ,business.industry ,Estimation theory ,Brain ,Pattern recognition ,Bayes Theorem ,Image Enhancement ,White Matter ,medicine.anatomical_structure ,Diffusion Magnetic Resonance Imaging ,Artificial intelligence ,business ,Algorithm ,computer ,030217 neurology & neurosurgery ,Algorithms ,Diffusion MRI - Abstract
We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements. more...
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- 2017
22. Estimation of the CSA-ODF using Bayesian compressed sensing of multi-shell HARDI
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Christophe Lenglet, Steen Moeller, Essa Yacoub, Lawrence Carin, Guillermo Sapiro, Julio M. Duarte-Carvajalino, Junqian Xu, and Kamil Ugurbil
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Computer science ,business.industry ,Orientation (computer vision) ,Dirichlet process ,Reduction (complexity) ,Bayes' theorem ,Redundancy (information theory) ,Sampling (signal processing) ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Artificial intelligence ,business ,Algorithm ,Data compression ,Diffusion MRI - Abstract
Purpose Diffusion MRI provides important information about the brain white matter structures and has opened new avenues for neuroscience and translational research. However, acquisition time needed for advanced applications can still be a challenge in clinical settings. There is consequently a need to accelerate diffusion MRI acquisitions. Methods A multi-task Bayesian compressive sensing (MT-BCS) framework is proposed to directly estimate the constant solid angle orientation distribution function (CSA-ODF) from under-sampled (i.e., accelerated image acquisition) multi-shell high angular resolution diffusion imaging (HARDI) datasets, and accurately recover HARDI data at higher resolution in q-space. The proposed MT-BCS approach exploits the spatial redundancy of the data by modeling the statistical relationships within groups (clusters) of diffusion signal. This framework also provides uncertainty estimates of the computed CSA-ODF and diffusion signal, directly computed from the compressive measurements. Experiments validating the proposed framework are performed using realistic multi-shell synthetic images and in vivo multi-shell high angular resolution HARDI datasets. Results Results indicate a practical reduction in the number of required diffusion volumes (q-space samples) by at least a factor of four to estimate the CSA-ODF from multi-shell data. Conclusion This work presents, for the first time, a multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions. It demonstrates improvement of the quality of acquired datasets by means of CS de-noising, and accurate estimation of the CSA-ODF, as well as enables a reduction in the acquisition time by a factor of two to four, especially when “staggered” q-space sampling schemes are used. The proposed MT-BCS framework can naturally be combined with parallel MR imaging to further accelerate HARDI acquisitions. Magn Reson Med 72:1471–1485, 2014. © 2013 Wiley Periodicals, Inc. more...
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- 2013
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23. Design of multishell sampling schemes with uniform coverage in diffusion MRI
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Rachid Deriche, Guillermo Sapiro, Emmanuel Caruyer, and Christophe Lenglet
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Orientation (computer vision) ,Design of experiments ,Sampling (statistics) ,Discrete Fourier transform ,030218 nuclear medicine & medical imaging ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,Nuclear magnetic resonance ,law ,Radiology, Nuclear Medicine and imaging ,Cartesian coordinate system ,Angular resolution ,Diffusion (business) ,Algorithm ,030217 neurology & neurosurgery ,Diffusion MRI ,Mathematics - Abstract
PURPOSE: In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal. Alternatively, it is possible to directly reconstruct the orientation distribution function in q-ball imaging, providing so-called high angular resolution diffusion imaging. In between these two techniques, acquisitions on several spheres in q-space offer an interesting trade-off between the angular resolution and the radial information gathered in diffusion MRI. A careful design is central in the success of multishell acquisition and reconstruction techniques. METHODS: The design of acquisition in multishell is still an open and active field of research, however. In this work, we provide a general method to design multishell acquisition with uniform angular coverage. This method is based on a generalization of electrostatic repulsion to multishell. RESULTS: We evaluate the impact of our method using simulations, on the angular resolution in one and two bundles of fiber configurations. Compared to more commonly used radial sampling, we show that our method improves the angular resolution, as well as fiber crossing discrimination. DISCUSSION: We propose a novel method to design sampling schemes with optimal angular coverage and show the positive impact on angular resolution in diffusion MRI. more...
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- 2013
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24. Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE
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Edward J. Auerbach, David A. Feinberg, Kamil Ugurbil, Saad Jbabdi, Jesper L. R. Andersson, Lawrence L. Wald, Junqian Xu, Christophe Lenglet, Stamatios N. Sotiropoulos, Kawin Setsompop, Timothy E.J. Behrens, Essa Yacoub, and Steen Moeller more...
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Orientation (computer vision) ,Computer science ,business.industry ,Reconstruction algorithm ,Iterative reconstruction ,Noise floor ,Signal ,Signal-to-noise ratio ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Artificial intelligence ,business ,Tractography ,Diffusion MRI - Abstract
Purpose: To examine the effects of the reconstruction algorithm of magnitude images from multi-channel diffusion MRI on fibre orientation estimation. Theory and Methods: It is well established that the method used to combine signals from different coil elements in multi-channel MRI can have an impact on the properties of the reconstructed magnitude image. Utilising a root-sum-of-squares (RSoS) approach results in a magnitude signal that follows an effective non-central-distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest. Results: In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fibre orientations, both for model-based and model-free approaches, when modern 32-channel coils are employed. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, RSoS can cause excessive overfitting and reduced precision in orientation estimation compared to the SENSE-based approach. Conclusion: These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multi-channel receiver coils for diffusion MRI acquisition. more...
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- 2013
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25. Motion Detection in Diffusion MRI via Online ODF Estimation
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Guillermo Sapiro, Rachid Deriche, Christophe Lenglet, Emmanuel Caruyer, Iman Aganj, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratory for Information and Decision Systems - Massachusetts Institute of Technology (LIDS), Massachusetts Institute of Technology (MIT), Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Department of Electrical and Computer Engineering [Durham] (ECE), Duke University [Durham], Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Laboratory for Information and Decision Systems, Aganj, Iman, and Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS) more...
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lcsh:Medical physics. Medical radiology. Nuclear medicine ,lcsh:Medical technology ,Article Subject ,Computer science ,lcsh:R895-920 ,ODF ,Diffusion MRI ,030218 nuclear medicine & medical imaging ,Scan time ,Motion ,03 medical and health sciences ,0302 clinical medicine ,Robustness (computer science) ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Radiology, Nuclear Medicine and imaging ,Computer vision ,business.industry ,Orientation Distribution Function ,Solid angle ,Motion detection ,Kalman filter ,3. Good health ,Distribution function ,lcsh:R855-855.5 ,Likelihood-ratio test ,Artificial intelligence ,business ,Kalman Filter ,030217 neurology & neurosurgery ,Research Article - Abstract
The acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provides real-time feedback throughout the acquisition process. In this article, the Kalman filter is first adapted to the reconstruction of the ODF in constant solid angle. Then, a method called STAR (STatistical Analysis of Residuals) is presented and applied to the online detection of motion in high angular resolution diffusion images. Compared to existing techniques, this method is image based and is built on top of a Kalman filter. Therefore, it introduces no additional scan time and does not require additional hardware. The performance of STAR is tested on simulated and real data and compared to the classical generalized likelihood ratio test. Successful detection of small motion is reported (rotation under 2°) with no delay and robustness to noise., National Institutes of Health (U.S.) (NIH grant Grant P41 RR008079), National Institutes of Health (U.S.) (NIH grant P41 EB015894), National Institutes of Health (U.S.) (NIH grant P30 NS057091), National Institutes of Health (U.S.) (Human Connectome Project U54 MH091657), United States. Air Force Office of Scientific Research (NSSEFF), National Science Foundation (U.S.), United States. Army Research Office, United States. Defense Advanced Research Projects Agency, United States. National Geospatial-Intelligence Agency, France. Agence nationale de la recherche (ANR NucleiPark), Institut national de recherche en informatique et en automatique (France), National Institutes of Health (U.S.) (Human Connectome Project, Grant R01 EB008432) more...
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- 2013
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26. Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI
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Jung Who Nam, Hamza Farooq, Tryphon T. Georgiou, Junqian Xu, Essa Yacoub, Christophe Lenglet, and Daniel F. Keefe
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Computer science ,Anisotropic diffusion ,Models, Neurological ,Bioengineering ,Corpus callosum ,Article ,030218 nuclear medicine & medical imaging ,Corpus Callosum ,White matter ,03 medical and health sciences ,0302 clinical medicine ,Data acquisition ,Models ,medicine ,Humans ,Fiber ,Multidisciplinary ,Orientation (computer vision) ,Neurosciences ,Microstructure ,White Matter ,Axons ,Other Physical Sciences ,medicine.anatomical_structure ,Diffusion Magnetic Resonance Imaging ,Networking and Information Technology R&D ,Networking and Information Technology R&D (NITRD) ,Neurological ,Biomedical Imaging ,Biochemistry and Cell Biology ,Biological system ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data. more...
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- 2016
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27. Brain Tissue Micro-Structure Imaging from Diffusion MRI Using Least Squares Variable Separation
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Essa Yacoub, Christophe Lenglet, Tryphon T. Georgiou, Hamza Farooq, and Junqian Xu
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Computational complexity theory ,Estimation theory ,Computer science ,Least squares ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Nuclear magnetic resonance ,Search algorithm ,Non-linear least squares ,Hyperparameter optimization ,Curve fitting ,Algorithm ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
We introduce a novel data fitting procedure of multi compartment models of the brain white matter for diffusion MRI (dMRI) data. These biophysical models aim to characterize important micro-structure quantities like axonal radius, density and orientations. In order to describe the underlying tissue properties, a variety of models for intra-/extra-axonal diffusion signals have been proposed. Combinations of these analytic models are used to predict the diffusion MRI signal in multi-compartment settings. However, parameter estimation from these multi-compartment models is an ill-posed problem. Consequently, many existing fitting algorithms either rely on an initial grid search to find a good start point, or have strong assumptions like single fiber orientation to estimate some of these parameters from simpler models like the diffusion tensor (DT). In both cases, there is a trade-off between computational complexity and accuracy of the estimated parameters. Here, we describe a novel algorithm based on the separation of the Nonlinear Least Squares (NLLS) fitting problem, via Variable Projection Method , to search for non-linearly and linearly entering parameters independently. We use stochastic global search algorithms to find a global minimum, while estimating non-linearly entering parameters. The approach is independent of any starting point, and does not rely on estimates from simpler models. We show that the suggested algorithm is faster than algorithms involving grid search, and its greater accuracy and robustness are demonstrated on synthetic as well as ex-/in-vivo data. more...
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- 2016
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28. Joint brain connectivity estimation from diffusion and functional MRI data
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Christophe Lenglet, Shu-Hsien Chu, and Keshab K. Parhi
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Human Connectome Project ,medicine.diagnostic_test ,business.industry ,Computer science ,Node (networking) ,Functional connectivity ,Pattern recognition ,Statistical model ,Grey matter ,Machine learning ,computer.software_genre ,Independent component analysis ,White matter ,medicine.anatomical_structure ,medicine ,Artificial intelligence ,Functional magnetic resonance imaging ,business ,computer ,Tractography ,Diffusion MRI - Abstract
Estimating brain wiring patterns is critical to better understand the brain organization and function. Anatomical brain connectivity models axonal pathways, while the functional brain connectivity characterizes the statistical dependencies and correlation between the activities of various brain regions. The synchronization of brain activity can be inferred through the variation of blood-oxygen-level dependent (BOLD) signal from functional MRI (fMRI) and the neural connections can be estimated using tractography from diffusion MRI (dMRI). Functional connections between brain regions are supported by anatomical connections, and the synchronization of brain activities arises through sharing of information in the form of electro-chemical signals on axon pathways. Jointly modeling fMRI and dMRI data may improve the accuracy in constructing anatomical connectivity as well as functional connectivity. Such an approach may lead to novel multimodal biomarkers potentially able to better capture functional and anatomical connectivity variations. We present a novel brain network model which jointly models the dMRI and fMRI data to improve the anatomical connectivity estimation and extract the anatomical subnetworks associated with specific functional modes by constraining the anatomical connections as structural supports to the functional connections. The key idea is similar to a multi-commodity flow optimization problem that minimizes the cost or maximizes the efficiency for flow configuration and simultaneously fulfills the supply-demand constraint for each commodity. In the proposed network, the nodes represent the grey matter (GM) regions providing brain functionality, and the links represent white matter (WM) fiber bundles connecting those regions and delivering information. The commodities can be thought of as the information corresponding to brain activity patterns as obtained for instance by independent component analysis (ICA) of fMRI data. The concept of information flow is introduced and used to model the propagation of information between GM areas through WM fiber bundles. The link capacity , i.e., ability to transfer information, is characterized by the relative strength of fiber bundles, e.g., fiber count gathered from the tractography of dMRI data. The node information demand is considered to be proportional to the correlation between neural activity at various cortical areas involved in a particular functional mode (e.g. visual, motor, etc.). These two properties lead to the link capacity and node demand constraints in the proposed model. Moreover, the information flow of a link cannot exceed the demand from either end node. This is captured by the feasibility constraints . Two different cost functions are considered in the optimization formulation in this paper. The first cost function, the reciprocal of fiber strength represents the unit cost for information passing through the link. In the second cost function, a min-max (minimizing the maximal link load) approach is used to balance the usage of each link. Optimizing the first cost function selects the pathway with strongest fiber strength for information propagation. In the second case, the optimization procedure finds all the possible propagation pathways and allocates the flow proportionally to their strength. Additionally, a penalty term is incorporated with both the cost functions to capture the possible missing and weak anatomical connections. With this set of constraints and the proposed cost functions, solving the network optimization problem recovers missing and weak anatomical connections supported by the functional information and provides the functional-associated anatomical subnetworks. Feasibility is demonstrated using realistic diffusion and functional MRI phantom data. It is shown that the proposed model recovers the maximum number of true connections, with fewest number of false connections when compared with the connectivity derived from a joint probabilistic model using the expectation-maximization (EM) algorithm presented in a prior work. We also apply the proposed method to data provided by the Human Connectome Project (HCP). more...
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- 2015
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29. Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI
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Guillermo Sapiro, Christophe Lenglet, Stamatios N. Sotiropoulos, Julio M. Duarte-Carvajalino, and Pramod Kumar Pisharady
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Computer science ,business.industry ,Fiber (mathematics) ,Partial volume ,Pattern recognition ,Bayesian inference ,Article ,White matter ,Compressed sensing ,medicine.anatomical_structure ,Neuroimaging ,medicine ,Artificial intelligence ,Deconvolution ,Focus (optics) ,business ,Parametric statistics ,Diffusion MRI - Abstract
The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations. more...
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- 2015
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30. Diffusion Tensor Imaging
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Christophe Lenglet
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Anisotropic diffusion ,Computation ,Fractional anisotropy ,Mathematical properties ,Geometry ,Statistical physics ,Ellipsoid ,Eigenvalues and eigenvectors ,Tractography ,Mathematics ,Diffusion MRI - Abstract
This article provides an overview of the origin, computation, properties, and applications of the diffusion tensor model. It details how the diffusion tensor formalism was initially introduced to quantify anisotropic diffusion of water molecules in the brain and to model diffusion-weighted measurements from magnetic resonance imaging experiments. It outlines the various computational options to reliably estimate the diffusion tensor. It finally describes the mathematical properties of the tensor model, how they relate to tissue microstructure, and how they can be used for neuroscience and clinical applications. more...
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- 2015
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31. Structure tensor analysis of serial optical coherence scanner images for mapping fiber orientations and tractography in the brain
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Taner Akkin, Hui Wang, and Christophe Lenglet
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Scanner ,Computer science ,Research Papers: Imaging ,Biomedical Engineering ,Brain mapping ,Structure tensor ,Biomaterials ,Optics ,Optical coherence tomography ,medicine ,Image Processing, Computer-Assisted ,Animals ,Computer vision ,Anisotropy ,Brain Mapping ,medicine.diagnostic_test ,business.industry ,Brain ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,Rats ,Optical axis ,Diffusion Tensor Imaging ,Artificial intelligence ,business ,Tomography, Optical Coherence ,Diffusion MRI ,Tractography - Abstract
Quantitative investigations of fiber orientation and structural connectivity at microscopic resolution have led to great challenges for current neuroimaging techniques. Here, we present a structure tensor (ST) analysis of ex vivo rat brain images acquired by a multicontrast (MC) serial optical coherence scanner. The ST considers the gradients of images in local neighbors to generate a matrix whose eigen-decomposition can estimate the local features such as the edges, anisotropy, and orientation of tissue constituents. This computational analysis is applied on the conventional- and polarization-based contrasts of optical coherence tomography. The three-dimensional (3-D) fiber orientation maps are computed from the image stacks of sequential scans both at mesoresolution for a global view and at high-resolution for the details. The computational orientation maps demonstrate a good agreement with the optic axis orientation contrast which measures the in-plane fiber orientation. Moreover, tractography is implemented using the directional information extracted from the 3-D ST. The study provides a unique opportunity to leverage MC high-resolution information to map structural connectivity of the whole brain. more...
- Published
- 2014
32. SEMI-AUTOMATIC SEGMENTATION OF BRAIN SUBCORTICAL STRUCTURES FROM HIGH-FIELD MRI
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Jinyoung Kim, Guillermo Sapiro, Yuval Duchin, Christophe Lenglet, and Noam Harel
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Scanner ,Computer science ,Image quality ,Image processing ,Article ,Basal Ganglia ,Health Information Management ,Thalamus ,medicine ,Image Processing, Computer-Assisted ,Humans ,Segmentation ,Computer vision ,Electrical and Electronic Engineering ,Semiautomatic segmentation ,medicine.diagnostic_test ,business.industry ,Brain ,Magnetic resonance imaging ,Real-time MRI ,Magnetic Resonance Imaging ,Computer Science Applications ,Artificial intelligence ,business ,Algorithms ,Biotechnology ,Diffusion MRI - Abstract
Volumetric segmentation of subcortical structures, such as the basal ganglia and thalamus, is necessary for noninvasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. This paper presents a semiautomatic segmentation system exploiting the superior image quality of ultrahigh field (7 T) MRI. The proposed approach utilizes the complementary edge information in the multiple structural MRI modalities. It combines optimally selected two modalities from susceptibility-weighted, T2-weighted, and diffusion MRI, and introduces a tailored new edge indicator function. In addition to this, we employ prior shape and configuration knowledge of the subcortical structures in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining oversegmentation at their borders with a nonoverlapping penalty. Several experiments with data acquired on a 7 T MRI scanner demonstrate the feasibility and power of the approach for the segmentation of basal ganglia components critical for neurosurgery applications such as deep brain stimulation surgery. more...
- Published
- 2014
33. Automatic clustering and population analysis of white matter tracts using maximum density paths
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Julio E. Villalon-Reina, Iman Aganj, Christophe Lenglet, Arthur W. Toga, Nicholas G. Martin, Margaret J. Wright, Greig I. de Zubicaray, Katie L. McMahon, Paul M. Thompson, Neda Jahanshad, Shantanu H. Joshi, Gautam Prasad, and Guillermo Sapiro more...
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Male ,Cognitive Neuroscience ,Population ,Article ,White matter ,Young Adult ,Nerve Fibers ,Fractional anisotropy ,Neural Pathways ,medicine ,Image Processing, Computer-Assisted ,Cluster Analysis ,Humans ,Computer vision ,education ,Cluster analysis ,Spatial analysis ,Mathematics ,education.field_of_study ,business.industry ,Reproducibility of Results ,Pattern recognition ,White Matter ,medicine.anatomical_structure ,Diffusion Tensor Imaging ,Neurology ,Maximum density ,Anisotropy ,Female ,Artificial intelligence ,business ,Algorithms ,Diffusion MRI ,Tractography - Abstract
We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches. more...
- Published
- 2013
34. Magnetic Resonance Field Strength Effects on Diffusion Measures and Brain Connectivity Networks
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Kelvin O. Lim, Bryon A. Mueller, Liang Zhan, Neda Jahanshad, Christophe Lenglet, Guillermo Sapiro, Paul M. Thompson, Essa Yacoub, Yan Jin, Arthur W. Toga, Kamil Ugurbil, and Noam Harel
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Brain Mapping ,medicine.diagnostic_test ,General Neuroscience ,Brain ,Magnetic resonance imaging ,Field strength ,Original Articles ,Brain mapping ,Nuclear magnetic resonance ,Diffusion Magnetic Resonance Imaging ,Signal-to-noise ratio (imaging) ,Fractional anisotropy ,Image Interpretation, Computer-Assisted ,Neural Pathways ,medicine ,Humans ,Diffusion (business) ,Nerve Net ,Psychology ,Neuroscience ,Tractography ,Diffusion MRI - Abstract
The quest to map brain connectivity is being pursued worldwide using diffusion imaging, among other techniques. Even so, we know little about how brain connectivity measures depend on the magnetic field strength of the scanner. To investigate this, we scanned 10 healthy subjects at 7 and 3 tesla-using 128-gradient high-angular resolution diffusion imaging. For each subject and scan, whole-brain tractography was used to estimate connectivity between 113 cortical and subcortical regions. We examined how scanner field strength affects (i) the signal-to-noise ratio (SNR) of the non-diffusion-sensitized reference images (b(0)); (ii) diffusion tensor imaging (DTI)-derived fractional anisotropy (FA), mean, radial, and axial diffusivity (MD/RD/AD), in atlas-defined regions; (iii) whole-brain tractography; (iv) the 113 × 113 brain connectivity maps; and (v) five commonly used network topology measures. We also assessed effects of the multi-channel reconstruction methods (sum-of-squares, SOS, at 7T; adaptive recombine, AC, at 3T). At 7T with SOS, the b0 images had 18.3% higher SNR than with 3T-AC. FA was similar for most regions of interest (ROIs) derived from an online DTI atlas (ICBM81), but higher at 7T in the cerebral peduncle and internal capsule. MD, AD, and RD were lower at 7T for most ROIs. The apparent fiber density between some subcortical regions was greater at 7T-SOS than 3T-AC, with a consistent connection pattern overall. Suggesting the need for caution, the recovered brain network was apparently more efficient at 7T, which cannot be biologically true as the same subjects were assessed. Care is needed when comparing network measures across studies, and when interpreting apparently discrepant findings. more...
- Published
- 2013
35. A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation
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Noam Harel, Guillermo Sapiro, Julio M. Duarte-Carvajalino, and Christophe Lenglet
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Registration ,Computer science ,Population ,Image registration ,computer.software_genre ,lcsh:RC321-571 ,030218 nuclear medicine & medical imaging ,Diffusion ,03 medical and health sciences ,0302 clinical medicine ,Voxel ,Fractional anisotropy ,Methods Article ,Computer vision ,Tensor ,education ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,education.field_of_study ,business.industry ,General Neuroscience ,Fiber Orientation ,Angular Interpolation ,Computer Science::Computer Vision and Pattern Recognition ,FMRIB Software Library ,Artificial intelligence ,Affine transformation ,business ,Algorithm ,computer ,030217 neurology & neurosurgery ,Neuroscience ,Diffusion MRI - Abstract
Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis. more...
- Published
- 2013
- Full Text
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36. Differential information content in staggered multiple shell hardi measured by the tensor distribution function
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Liang Zhan, Paul M. Thompson, Christophe Lenglet, Arthur W. Toga, Alex D. Leow, Iman Aganj, Guillermo Sapiro, Essa Yacoub, and Noam Harel
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Diffusion imaging ,Distribution function ,Fiber crossing ,Differential information ,Mathematical analysis ,Entropy (information theory) ,Geometry ,Image resolution ,Eigenvalues and eigenvectors ,Diffusion MRI ,Mathematics - Abstract
Diffusion tensor imaging has accelerated the study of brain connectivity, but single-tensor diffusion models are too simplistic to model fiber crossing and mixing. Hybrid diffusion imaging (HYDI) samples the radial and angular structure of local diffusion on multiple spherical shells in q-space, combining the high SNR and CNR achievable at low and high b-values, respectively. We acquired and analyzed human multi-shell HARDI at ultra-high field-strength (7 Tesla; b=1000, 2000, 3000 s/mm2). In experiments with the tensor distribution function (TDF), the b-value affected the intrinsic uncertainty for estimating component fiber orientations and their diffusion eigenvalues. We computed orientation density functions by least-squares fitting in multiple HARDI shells simultaneously. Within the range examined, higher b-values gave improved orientation estimates but poorer eigenvalue estimates; lower b-values showed opposite strengths and weaknesses. Combining these strengths, multiple-shell HARDI, especially with staggered angular sampling, outperformed single-shell scanning protocols, even when overall scanning time was held constant. more...
- Published
- 2011
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37. Reducing structural variation to determine the genetics of white matter integrity across hemispheres - A DTI study of 100 twins
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Paul M. Thompson, Greig I. de Zubicaray, Yi-Yu Chou, Christophe Lenglet, Arthur W. Toga, Margaret J. Wright, Marina Barysheva, Neda Jahanshad, Katie L. McMahon, Agatha D. Lee, Guillermo Sapiro, Natasha Lepore, and Caroline Brun more...
- Subjects
Fiber (mathematics) ,Functional specialization ,Biology ,Neurophysiology ,Article ,Lateralization of brain function ,White matter ,Structural variation ,Nuclear magnetic resonance ,medicine.anatomical_structure ,nervous system ,medicine ,Brain asymmetry ,Neuroscience ,Diffusion MRI - Abstract
Studies of cerebral asymmetry can open doors to understanding the functional specialization of each brain hemisphere, and how this is altered in disease. Here we examined hemispheric asymmetries in fiber architecture using diffusion tensor imaging (DTI) in 100 subjects, using high-dimensional fluid warping to disentangle shape differences from measures sensitive to myelination. Confounding effects of purely structural asymmetries were reduced by using co-registered structural images to fluidly warp 3D maps of fiber characteristics (fractional and geodesic anisotropy) to a structurally symmetric minimal deformation template (MDT). We performed a quantitative genetic analysis on 100 subjects to determine whether the sources of the remaining signal asymmetries were primarily genetic or environmental. A twin design was used to identify the heritable features of fiber asymmetry in various regions of interest, to further assist in the discovery of genes influencing brain micro-architecture and brain lateralization. Genetic influences and left/right asymmetries were detected in the fiber architecture of the frontal lobes, with minor differences depending on the choice of registration template. more...
- Published
- 2009
- Full Text
- View/download PDF
38. Mathematical methods for diffusion MRI processing
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Jennifer S.W. Campbell, Paul M. Thompson, Kaleem Siddiqi, Demian Wassermann, Alfred Anwander, G.B. Pike, Gloria Haro, Rachid Deriche, Peter Savadjiev, Maxime Descoteaux, Christophe Lenglet, and Guillermo Sapiro more...
- Subjects
Cognitive Neuroscience ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Image Processing, Computer-Assisted ,Humans ,Segmentation ,Computer vision ,Diffusion (business) ,Mathematics ,medicine.diagnostic_test ,Mathematical model ,business.industry ,Nonlinear dimensionality reduction ,Brain ,Computational Biology ,Magnetic resonance imaging ,Models, Theoretical ,Diffusion Magnetic Resonance Imaging ,Neurology ,Computer Science::Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Focus (optics) ,030217 neurology & neurosurgery ,Algorithms ,Tractography ,Diffusion MRI - Abstract
In this article, we review recent mathematical models and computational methods for the processing of diffusion Magnetic Resonance Images, including state-of-the-art reconstruction of diffusion models, cerebral white matter connectivity analysis, and segmentation techniques. We focus on Diffusion Tensor Images (DTI) and Q-Ball Images (QBI). more...
- Published
- 2009
39. Diffusion tensor sharpening improves white matter tractography
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Christophe Lenglet, Maxime Descoteaux, and Rachid Deriche
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Physics ,business.industry ,Fiber (mathematics) ,Sharpening ,Computer vision ,Tensor ,Artificial intelligence ,Deconvolution ,Diffusion (business) ,business ,Anisotropy ,Algorithm ,Tractography ,Diffusion MRI - Abstract
Diffusion Tensor Imaging (DTI) is currently a widespread technique to infer white matter architecture in the human brain. An important application of DTI is to understand the anatomical coupling between functional cortical regions of the brain. To solve this problem, anisotropy maps are insufficient and fiber tracking methods are used to obtain the main fibers. While the diffusion tensor (DT) is important to obtain anisotropy maps and apparent diffusivity of the underlying tissue, fiber tractography using the full DT may result in diffusive tracking that leaks into unexpected regions. Sharpening is thus of utmost importance to obtain complete and accurate tracts. In the tracking literature, only heuristic methods have been proposed to deal with this problem. We propose a new tensor sharpening transform. Analogously to the general issue with the diffusion and fiberOrientation Distribution Function (ODF) encountered when working with High Angular Resolution Diffusion Imaging (HARDI), we show how to transform the diffusion tensors into so-called fiber tensors. We demonstrate that this tensor transform is a natural pre-processing task when one is interested in fiber tracking. It also leads to a dramatic improvement of the tractography results obtained by front propagation techniques on the full diffusion tensor. We compare and validate sharpening and tracking results on synthetic data and on known fiber bundles in the human brain. more...
- Published
- 2007
- Full Text
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40. A Statistical Framework for DTI Segmentation
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Mikael Rousson, Rachid Deriche, and Christophe Lenglet
- Subjects
Mathematical optimization ,Kullback–Leibler divergence ,business.industry ,Scale-space segmentation ,Pattern recognition ,Image segmentation ,Euclidean distance ,Computer Science::Computer Vision and Pattern Recognition ,Metric (mathematics) ,Artificial intelligence ,Tensor ,business ,Divergence (statistics) ,Mathematics ,Diffusion MRI - Abstract
We address the problem of the segmentation of cerebral white matter structures from diffusion tensor images (DTI). DTI can be estimated from a set of diffusion weighted images and provides tensor-valued images where each voxel is assigned with a 3 /spl times/ 3 symmetric, positive-definite matrix. As we will show in this paper, the definition of a dissimilarity measure and statistics between tensors is a non trivial task which must be carefully tackled. We claim that, by using the differential geometrical properties of the manifold of multivariate normal distributions, it is possible to improve the quality of the segmentation obtained with other dissimilarity measures such as the Euclidean distance or the Kullback-Leibler divergence. Our goal is to prove that the choice of this probability metric has a deep impact on the tensor statistics and, hence, on the achieved results. We introduce a variational formulation to estimate the optimal segmentation of a diffusion tensor image. We show how to estimate diffusion tensors statistics for three different probability metrics and evaluate their respective performances. We validate and compare the results obtained on synthetic and real datasets. more...
- Published
- 2006
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41. Variational Approaches to the Estimation, Regularizatinn and Segmentation of Diffusion Tensor Images
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Christophe Lenglet, Mikael Rousson, D. Tschumpelé, and Rachid Deriche
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business.industry ,Anisotropic diffusion ,Pattern recognition ,Regularization (mathematics) ,Tensor field ,Fractional anisotropy ,Segmentation ,Computer vision ,Artificial intelligence ,Diffusion (business) ,business ,Tractography ,Mathematics ,Diffusion MRI - Abstract
Diffusion magnetic resonance imaging probes and quantifies the anisotropic diffusion of water molecules in biological tissues, making it possible to non-invasively infer the architecture of the underlying structure. In this chapter, we present a set of new techniques for the robust estimation and regularization of diffusion tensor images (DTI) as well as a novel statistical framework for the segmentation of cerebral white matter structures from this type of dataset. Numerical experiments conducted on real diffusion weighted MRI illustrate the techniques and exhibit promising results. more...
- Published
- 2005
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42. DT-MRI estimation, regularization and fiber tractography
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David Tschumperlé, Christophe Lenglet, and Rachid Deriche
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Nuclear magnetic resonance ,business.industry ,Anisotropic diffusion ,Computer science ,Cerebral white matter ,Regularization (physics) ,Physics::Medical Physics ,Fiber tractography ,Pattern recognition ,Artificial intelligence ,business ,Tractography ,Diffusion MRI - Abstract
Diffusion tensor MRI probes and quantifies the anisotropic diffusion of water molecules in biological tissues, making it possible to non-invasively infer the architecture of the underlying structures. In this article, we present a set of new techniques for the estimation and regularization of diffusion tensors MRI datasets as well as a novel approach to the cerebral white matter connectivity mapping. Numerical experimentations conducted on real diffusion weighted MRI will exhibit promising results. more...
- Published
- 2005
- Full Text
- View/download PDF
43. A Riemannian Approach to Diffusion Tensor Images Segmentation
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Rachid Deriche, Christophe Lenglet, Kamil Ugurbil, Mikael Rousson, Olivier Faugeras, and Stéphane Lehéricy
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Active contour model ,symbols.namesake ,Level set ,Geodesic ,Gaussian ,Mathematical analysis ,symbols ,Segmentation ,Multivariate normal distribution ,Focus (optics) ,Algorithm ,Diffusion MRI ,Mathematics - Abstract
We address the problem of the segmentation of cerebral white matter structures from diffusion tensor images. Our approach is grounded on the theoretically well-founded differential geometrical properties of the space of multivariate normal distributions. We introduce a variational formulation, in the level set framework, to estimate the optimal segmentation according to the following hypothesis: Diffusion tensors exhibit a Gaussian distribution in the different partitions. Moreover, we must respect the geometric constraints imposed by the interfaces existing among the cerebral structures and detected by the gradient of the diffusion tensor image. We validate our algorithm on synthetic data and report interesting results on real datasets. We focus on two structures of the white matter with different properties and respectively known as the corpus callosum and the corticospinal tract. more...
- Published
- 2005
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44. Inferring White Matter Geometry from Diffusion Tensor MRI: Application to Connectivity Mapping
- Author
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Rachid Deriche, Olivier Faugeras, and Christophe Lenglet
- Subjects
Geodesic ,Anisotropic diffusion ,Mathematical analysis ,Metric (mathematics) ,Geometry ,Riemannian manifold ,Diffusion (business) ,Exponential map (Riemannian geometry) ,Brownian motion ,Mathematics ,Diffusion MRI - Abstract
We introduce a novel approach to the cerebral white matter connectivity mapping from diffusion tensor MRI. DT-MRI is the unique non-invasive technique capable of probing and quantifying the anisotropic diffusion of water molecules in biological tissues. We address the problem of consistent neural fibers reconstruction in areas of complex diffusion profiles with potentially multiple fibers orientations. Our method relies on a global modelization of the acquired MRI volume as a Riemannian manifold M and proceeds in 4 majors steps: First, we establish the link between Brownian motion and diffusion MRI by using the Laplace-Beltrami operator on M. We then expose how the sole knowledge of the diffusion properties of water molecules on M is sufficient to infer its geometry. There exists a direct mapping between the diffusion tensor and the metric of M. Next, having access to that metric, we propose a novel level set formulation scheme to approximate the distance function related to a radial Brownian motion on M. Finally, a rigorous numerical scheme using the exponential map is derived to estimate the geodesics of M, seen as the diffusion paths of water molecules. Numerical experimentations conducted on synthetic and real diffusion MRI datasets illustrate the potentialities of this global approach. more...
- Published
- 2004
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45. Level Set and Region Based Surface Propagation for Diffusion Tensor MRI Segmentation
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Christophe Lenglet, Rachid Deriche, and Mikael Rousson
- Subjects
Surface (mathematics) ,Field (physics) ,business.industry ,Multivariate normal distribution ,Pattern recognition ,Level set ,Segmentation ,Computer vision ,Artificial intelligence ,Anisotropy ,business ,Diffusion MRI ,Mathematics ,Tractography - Abstract
Diffusion Tensor Imaging (DTI) is a relatively new modality for human brain imaging. During the last years, this modality has become widely used in medical studies. Tractography is currently the favorite technique to characterize and analyse the structure of the brain white matter. Only a few studies have been proposed to group data of particular interest. Rather than working on extracted fibers or on an estimated scalar value accounting for anisotropy as done in other approaches, we propose to extend classical segmentation techniques based on surface evolution by considering region statistics defined on the full diffusion tensor field itself. A multivariate Gaussian is used to approximate the density of the components of diffusion tensor for each sub-region of the volume. We validate our approach on synthetical data and we show promising results on the extraction of the corpus callosum from a real dataset. more...
- Published
- 2004
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46. Segmentation of 3D Probability Density Fields by Surface Evolution: Application to Diffusion MRI
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Mikael Rousson, Christophe Lenglet, and Rachid Deriche
- Subjects
Active contour model ,medicine.diagnostic_test ,Computer science ,business.industry ,Magnetic resonance imaging ,Probability density function ,Pattern recognition ,computer.software_genre ,Level set ,Voxel ,medicine ,Segmentation ,Computer vision ,Information geometry ,Artificial intelligence ,business ,computer ,Diffusion MRI - Abstract
We propose an original approach for the segmentation of three-dimensional fields of probability density functions. This presents a wide range of applications in medical images processing, in particular for diffusion magnetic resonance imaging where each voxel is assigned with a function describing the average motion of water molecules. Being able to automatically extract relevant anatomical structures of the white matter, such as the corpus callosum, would dramatically improve our current knowledge of the cerebral connectivity as well as allow for their statistical analysis. Our approach relies on the use of the symmetrized Kullback-Leibler distance and on the modelization of its distribution over the subsets of interest in the volume. The variational formulation of the problem yields a level-set evolution converging toward the optimal segmentation. more...
- Published
- 2004
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47. Multi-subject Diffusion MRI Tractography via a Hough Transform Global Approach
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Iman Aganj, Ming Chang Chiang, Christophe Lenglet, Paul M. Thompson, and Guillermo Sapiro
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Neurology ,Computer science ,business.industry ,law ,Cognitive Neuroscience ,Subject (documents) ,Computer vision ,Artificial intelligence ,business ,Diffusion MRI ,Tractography ,Hough transform ,law.invention - Published
- 2009
- Full Text
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48. Generic acquisition protocol for quantitative MRI of the spinal cord
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René Labounek, Nawal Kinany, Michela Fratini, Laura Barlow, Markus Barth, P Wyss, Marco Battiston, Sean Mackey, Nyoman D. Kurniawan, Deborah Pareto, Kenneth A. Weber, Guillaume Gilbert, Robert L. Barry, Anna Pichiecchio, Eva Alonso-Ortiz, Matthew D. Budde, Mihael Abramovic, Alexandra Tinnermann, Giancarlo Germani, Dimitri Van De Ville, Falk Eippert, Anna J.E. Combes, Igor Nestrasil, Marios C. Yiannakas, Haleh Karbasforoushan, Julien Doyon, Cornelia Laule, Nicole Atcheson, Ali Khatibi, Nico Papinutto, Karla R. Epperson, Marek Dostál, Yazhuo Kong, Loan Mattera, Charley Gros, Paulo Loureiro de Sousa, Pierre-Gilles Henry, Alan C. Seifert, Richard G. Wise, Issei Fukunaga, Alexandru Foias, Shannon H. Kolind, Todd B. Parrish, Akifumi Hagiwara, Daniel Papp, Carina Arneitz, Virginie Callot, Christian Büchel, Maria Marcella Laganà, Tobias Leutritz, Slawomir Kusmia, Ferran Prados, Kevin S. Epperson, Marc J. Ruitenberg, Nikolaus Weiskopf, Kouhei Kamiya, Benjamin De Leener, Adam V. Dvorak, Giovanni Savini, Christine S. Law, Petr Kudlička, Paul Kuntke, Julien Cohen-Adad, Alex Rovira, Jan Valošek, Patrick Freund, George Tackley, Zachary A. Smith, Eloy Martinez-Heras, Maxime Descoteaux, Yaou Liu, Miloš Keřkovský, Joo Won Kim, Kristin P. O’Grady, Elisabeth Solana, Rebecca S. Samson, Junqian Xu, Alex K. Smith, Federico Giove, Maryam Seif, Claudia A. M. Wheeler-Kingshott, Tomáš Horák, James M. Joers, Francesco Grussu, Christophe Lenglet, Jürgen Finsterbusch, Sara Llufriu, Hagen H. Kitzler, Masaaki Hori, Yuichi Suzuki, Seth A. Smith, École Polytechnique de Montréal (EPM), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Mila - Quebec AI Institute, Swiss Paraplegic Research = Schweizer Paraplegiker-Stiftung, University of Queensland [Brisbane], University of British Columbia (UBC), Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Massachusetts Institute of Technology (MIT), University College of London [London] (UCL), Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Medical College of Wisconsin [Milwaukee] (MCW), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Vanderbilt University Medical Center [Nashville], Vanderbilt University [Nashville], CHU Sainte Justine [Montréal], Université de Sherbrooke (UdeS), Centre d'Imagerie Moléculaire de Sherbrooke, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Masaryk University and University Hospital Brno, McGill University = Université McGill [Montréal, Canada], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Stanford University School of Medicine [CA, USA], Universität Zürich [Zürich] = University of Zurich (UZH), IRCCS Santa Lucia Foundation, Juntendo University School of Medicine [Tokyo, Japan], University of Pavia, IRCCS Mondino Foundation, Philips Healthcare [Markham], Museo Storico della Fisica e Centro di Studi e Ricerche 'Enrico Fermi', Roma, Vall d'Hebron University Hospital [Barcelona], Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Central European Institute of Technology [Brno] (CEITEC MU), Brno University of Technology [Brno] (BUT), Toho University Omori Medical Center [Tokyo], The University of Tokyo (UTokyo), Northwestern University Feinberg School of Medicine, Stanford University, University of Birmingham [Birmingham], Icahn School of Medicine at Mount Sinai [New York] (MSSM), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva [Switzerland], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Chinese Academy of Sciences [Beijing] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), University of Oxford [Oxford], University of Southern Queensland (USQ), Cardiff University, Epilepsy Society [Buckinghamshire] (MRI Unit), Departments of Ophthalmology and Pediatrics, University of Minnesota, Minneapolis, MN, University of Minnesota [Twin Cities] (UMN), University Hospital Olomouc [Czech Republic], Capital University of Medical Sciences [Beijing] (CUMS), Universitat de Barcelona (UB), University of California [San Francisco] (UCSF), University of California, Feinberg School of Medicine, Northwestern University [Evanston], Universitat Oberta de Catalunya [Barcelona] (UOC), University of Oklahoma Health Sciences Center (OUHSC), Palacky University Olomouc, Leipzig University, University 'G. d'Annunzio' of Chieti-Pescara [Chieti and Pescara, Italy], Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Masaryk University [Brno] (MUNI), Università degli Studi di Pavia = University of Pavia (UNIPV), Enrico Fermi Center for Study and Research | Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Université de Genève = University of Geneva (UNIGE), University of Oxford, University of California [San Francisco] (UC San Francisco), University of California (UC), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), and Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS) more...
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Adult ,Male ,medicine.medical_specialty ,Computer science ,Neuroimaging ,Article ,General Biochemistry, Genetics and Molecular Biology ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Image Processing, Computer-Assisted ,medicine ,Humans ,NeuroPoly ,Medical physics ,health care economics and organizations ,Protocol (science) ,medicine.diagnostic_test ,technology, industry, and agriculture ,Healthy subjects ,Magnetic resonance imaging ,Spinal cord ,Magnetic Resonance Imaging ,White matter microstructure ,humanities ,3. Good health ,Acquisition Protocol ,medicine.anatomical_structure ,Spinal Cord ,[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition. more...
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49. Incremental gradient table for multiple Q-shells diffusion MRI
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Emmanuel Caruyer, Christophe Lenglet, Guillermo Sapiro, Rachid Deriche, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Department of Electrical and Computer Engineering [Minneapolis] (ECE), University of Minnesota [Twin Cities] (UMN), and Work partly funded by NIH (grants R01 EB008432, R01 EB007813, P41 RR008079, P30 NS057091), University of Minnesota Institute for Translational Neuroscience, CD-MRI INRIA Associate Team. more...
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diffusion mri ,experimental design ,sampling scheme ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,signal processing on the sphere - Abstract
International audience; Most studies on sampling optimality for diffusion MRI deal with single Q-shell acquisition. For single Q-shell acquisition, incremental gradient table has proved useful in clinical setup, where the subject is likely to move, or for online reconstruction. In this article, we propose a generalization of the electrostatic repulsion to generate gradient tables for multiple Q-shells acquisitions, designed for incremental reconstruction or processing of data prematurely aborted. more...
50. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?
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Helena Melero, Anupa Ambili Vijayakumari, Eduardo Caverzasi, Fang-Cheng Yeh, René Labounek, Daniel Bullock, Vivek Prabhakaran, Shaun Warrington, Ping Hong Yeh, Narciso López-López, Mavilde Arantes, Michael Lauricella, Katja Heuer, Vince D. Calhoun, Francisco Guerreiro Fernandes, Aristotle N. Voineskos, Fan Zhang, Claire E. Kelly, Sila Genc, Franco Pestilli, Giorgio M. Innocenti, Rujirutana Srikanchana, Erick J. Canales-Rodríguez, Jonathan Rafael-Patino, Alessandro Daducci, Philippe Karan, Christophe Lenglet, Michael Joseph, Garikoitz Lerma-Usabiaga, Jian Chen, Lucius S. Fekonja, Sarah R. Heilbronner, Yihao Xia, Lucas Roitman, Matteo Mancini, Cristina Granziera, Dogu Baran Aydogan, Stephen J. Wastling, Wataru Uchida, Sirio Cocozza, Kiran K. Seunarine, Eleftherios Garyfallidis, Drew Parker, Hojjatollah Azadbakht, Ragini Verma, Simona Schiavi, Laura Korobova, Giuseppe Pontillo, Masahiro Abe, Nikos Makris, Egidio D'Angelo, Cyril Poupon, Gabriel Girard, Jerome Joseph Maller, Ramón Aranda, Jerome Cochereau, Bennett A. Landman, François Rheault, Andrea Vázquez, Muhamed Barakovic, Gabrielle Grenier, Maria Petracca, Giovanni Savini, Louise Emsell, Colin B. Hansen, Elda Fischi-Gomez, Claudia A. M. Wheeler-Kingshott, José Paulo Andrade, Lidia Manzanedo, Emilio Sanz-Morales, Sjoerd B. Vos, Roza G. Bayrak, Mariano Rivera Meraz, Wei Tang, Yonggang Shi, Mathijs Raemaekers, Stefan Sunaert, Fernando Calamante, Stijn Michielse, Yang Zhan, Laura Mancini, Susana M. Silva, Josselin Houenou, Maxime Descoteaux, Chris A. Clark, Alberto De Luca, Rajikha Raja, Alexandra J. Golby, Bramsh Qamar Chandio, Ryan P. Cabeen, Vejay N. Vakharia, Javier Guaje, Amy Paulson, Laurent Petit, Igor Nestrasil, Adam W. Anderson, Ahmed Radwan, Edith Brignoni-Pérez, Pamela Guevara, Ángel Peña-Melián, Joseph Yuan-Mou Yang, Arthur W. Toga, Arnaud Attyé, Luis Concha, John S. Duncan, Yogesh Rathi, Navona Calarco, Mario Ocampo-Pineda, Nicolò Rolandi, Alexander Leemans, Hajer Nakua, Christina Andica, Marco Pizzolato, Yuya Saito, Lauren J. O'Donnell, Jon Haitz Legarreta, Thomas Welton, Chun-Hung Yeh, Štefánia Aulická, Fabien Almairac, Claude J. Bajada, Koji Kamagata, Vishwesh Nath, Chantal M. W. Tax, Alonso Ramirez-Manzanares, Jess E. Reynolds, Kurt G. Schilling, Thomas Yu, Hamied A. Haroon, Jean-Philippe Thiran, Veena A. Nair, Maxime Chamberland, Simone Sacco, Chiara Maffei, Jean-François Mangin, Colin D. McKnight, Andrew L. Alexander, Catherine Lebel, C. Roman, Nagesh Adluru, Fulvia Palesi, RS: MHeNs - R3 - Neuroscience, Neurochirurgie, Sherbrooke Connectivity Imaging Lab [Sherbrooke] (SCIL), Département d'informatique [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS)-Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Groupe d'imagerie neurofonctionnelle (GIN), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Hôpital Pasteur [Nice] (CHU), Université Côte d'Azur (UCA), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Evolution et ingénierie de systèmes dynamiques (SEED (UMR-S 1284/U 1284)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), ANR-19-CE45-0022,IFOPASUBA,Inférence d'atlas de faisceaux en U spécifiques à chaque motif du plissement cortical(2019), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP) more...
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Computer science ,[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging ,Image Processing ,FRACTIONAL ANISOTROPY ,Corpus callosum ,Computer-Assisted ,0302 clinical medicine ,Neural Pathways ,Image Processing, Computer-Assisted ,Arcuate fasciculus ,Segmentation ,Bundle segmentation ,Dissection ,Fiber pathways ,Tractography ,White matter ,IN-VIVO ,0303 health sciences ,05 social sciences ,Radiology, Nuclear Medicine & Medical Imaging ,MEAN DIFFUSIVITY ,3. Good health ,Diffusion Tensor Imaging ,medicine.anatomical_structure ,Neurology ,[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,Life Sciences & Biomedicine ,Algorithms ,RC321-571 ,FIBER PATHWAYS ,Cognitive Neuroscience ,Neuroimaging ,Neurosciences. Biological psychiatry. Neuropsychiatry ,Article ,050105 experimental psychology ,ANATOMICAL ACCURACY ,03 medical and health sciences ,TENSOR IMAGING TRACTOGRAPHY ,Fractional anisotropy ,medicine ,Humans ,0501 psychology and cognitive sciences ,030304 developmental biology ,Science & Technology ,business.industry ,Fiber (mathematics) ,[SCCO.NEUR]Cognitive science/Neuroscience ,External validation ,Neurosciences ,Pattern recognition ,ARCUATE FASCICULUS ,CORPUS-CALLOSUM ,PRINCIPAL EIGENVECTOR MEASUREMENTS ,Bundle ,Artificial intelligence ,Neurosciences & Neurology ,business ,DIFFUSION MRI ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Available online 22 August 2021. White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process. This work was conducted in part using the resources of the Ad- vanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the Na- tional Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universitéde Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk ł odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Cen- ter from the National Institute of Child Health and Human Develop- ment (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the sup- port of the Cluster of Excellence Matters of Activity. Image Space Mate- rial funded by the Deutsche Forschungsgemeinschaft (DFG, German Re- search Foundation) under Germany´s Excellence Strategy –EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group’s processing was performed using the University of Nottingham’s Augusta HPC service and the Pre- cision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CIN- TESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellow- ship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Sci- ence Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Re- search Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32- MH103213 to William Hetrick (Indiana University). CL is partly sup- ported by NIH grants P41 EB027061 and P30 NS076408 “Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children’s Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children’s Hospital Foundation, Murdoch Children’s Research Institute, The Uni- versity of Melbourne Department of Paediatrics, and the Victorian Gov- ernment’s Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222- E-182-001-MY3) for the support. LC acknowledges support from CONA- CYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Pro- gramme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID- FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. more...
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