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972 results on '"Egan, Gary F."'

1. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging

Author

Renton, Angela I., Dao, Thuy T., Johnstone, Tom, Civier, Oren, Sullivan, Ryan P., White, David J., Lyons, Paris, Slade, Benjamin M., Abbott, David F., Amos, Toluwani J., Bollmann, Saskia, Botting, Andy, Campbell, Megan E. J., Chang, Jeryn, Close, Thomas G., Dörig, Monika, Eckstein, Korbinian, Egan, Gary F., Evas, Stefanie, Flandin, Guillaume, Garner, Kelly G., Garrido, Marta I., Ghosh, Satrajit S., Grignard, Martin, Halchenko, Yaroslav O., Hannan, Anthony J., Heinsfeld, Anibal S., Huber, Laurentius, Hughes, Matthew E., Kaczmarzyk, Jakub R., Kasper, Lars, Kuhlmann, Levin, Lou, Kexin, Mantilla-Ramos, Yorguin-Jose, Mattingley, Jason B., Meier, Michael L., Morris, Jo, Narayanan, Akshaiy, Pestilli, Franco, Puce, Aina, Ribeiro, Fernanda L., Rogasch, Nigel C., Rorden, Chris, Schira, Mark M., Shaw, Thomas B., Sowman, Paul F., Spitz, Gershon, Stewart, Ashley W., Ye, Xincheng, Zhu, Judy D., Narayanan, Aswin, and Bollmann, Steffen

Published
2024
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5. Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data

Author

Sudarshan, Viswanath P., Upadhyay, Uddeshya, Egan, Gary F., Chen, Zhaolin, and Awate, Suyash P.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, such DNN methods focus on applications involving test data that match the statistical characteristics of the training data very closely and give little attention to evaluating the performance of these DNNs on new out-of-distribution (OOD) acquisitions. We propose a novel DNN formulation that models the (i) underlying sinogram-based physics of the PET imaging system and (ii) the uncertainty in the DNN output through the per-voxel heteroscedasticity of the residuals between the predicted and the high-quality reference images. Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. Results on in vivo simultaneous PET-MRI, and various forms of OOD data in PET-MRI, show the benefits of suDNN over the current state of the art, quantitatively and qualitatively., Comment: Accepted at Medical Image Analysis

Published
2021

6. A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility

Author

Abrams, Mathew Birdsall, Bjaalie, Jan G, Das, Samir, Egan, Gary F, Ghosh, Satrajit S, Goscinski, Wojtek J, Grethe, Jeffrey S, Kotaleski, Jeanette Hellgren, Ho, Eric Tatt Wei, Kennedy, David N, Lanyon, Linda J, Leergaard, Trygve B, Mayberg, Helen S, Milanesi, Luciano, Mouček, Roman, Poline, JB, Roy, Prasun K, Strother, Stephen C, Tang, Tong Boon, Tiesinga, Paul, Wachtler, Thomas, Wójcik, Daniel K, and Martone, Maryann E

Subjects
Networking and Information Technology R&D (NITRD), Neurosciences, Underpinning research, 1.5 Resources and infrastructure (underpinning), Responsible Consumption and Production, Reproducibility of Results, Neuroinformatics, Standards and best practices, FAIR principles, Standards organization, Neuroscience, INCF, INCF endorsement process, Biochemistry and Cell Biology, Neurology & Neurosurgery
Abstract

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

Published
2022

13. Consensus-based guidance for conducting and reporting multi-analyst studies

Author

Aczel, Balazs, Szaszi, Barnabas, Nilsonne, Gustav, van den Akker, Olmo R, Albers, Casper J, van Assen, Marcel ALM, Bastiaansen, Jojanneke A, Benjamin, Daniel, Boehm, Udo, Botvinik-Nezer, Rotem, Bringmann, Laura F, Busch, Niko A, Caruyer, Emmanuel, Cataldo, Andrea M, Cowan, Nelson, Delios, Andrew, van Dongen, Noah NN, Donkin, Chris, van Doorn, Johnny B, Dreber, Anna, Dutilh, Gilles, Egan, Gary F, Gernsbacher, Morton Ann, Hoekstra, Rink, Hoffmann, Sabine, Holzmeister, Felix, Huber, Juergen, Johannesson, Magnus, Jonas, Kai J, Kindel, Alexander T, Kirchler, Michael, Kunkels, Yoram K, Lindsay, D Stephen, Mangin, Jean-Francois, Matzke, Dora, Munafò, Marcus R, Newell, Ben R, Nosek, Brian A, Poldrack, Russell A, van Ravenzwaaij, Don, Rieskamp, Jörg, Salganik, Matthew J, Sarafoglou, Alexandra, Schonberg, Tom, Schweinsberg, Martin, Shanks, David, Silberzahn, Raphael, Simons, Daniel J, Spellman, Barbara A, St-Jean, Samuel, Starns, Jeffrey J, Uhlmann, Eric Luis, Wicherts, Jelte, and Wagenmakers, Eric-Jan

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Health Sciences, Consensus, Data Analysis, Datasets as Topic, Research, multi-analyst, metascience, statistical practice, science forum, expert consensus, analytical variability, None, medicine, neuroscience, none, Biochemistry and Cell Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

Published
2021

16. MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach

Author

Pawar, Kamlesh, Chen, Zhaolin, Shah, N. Jon, and Egan, Gary F.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Purpose: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts from MR images using a data-driven deep learning approach. Methods: A deep learning convolutional neural network (CNN) was developed to remove motion artefacts in brain MR images. A CNN was trained on simulated motion corrupted images to identify and suppress artefacts due to the motion. The network was an encoder-decoder CNN architecture where the encoder decomposed the motion corrupted images into a set of feature maps. The feature maps were then combined by the decoder network to generate a motion-corrected image. The network was tested on an unseen simulated dataset and an experimental, motion corrupted in vivo brain dataset. Results: The trained network was able to suppress the motion artefacts in the simulated motion corrupted images, and the mean percentage error in the motion corrected images was 2.69 % with a standard deviation of 0.95 %. The network was able to effectively suppress the motion artefacts from the experimental dataset, demonstrating the generalisation capability of the trained network. Conclusion: A novel and generic motion correction technique has been developed that can suppress motion artefacts from motion corrupted MR images. The proposed technique is a standalone post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for a multitude of MR sequences.

Published
2018
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17. Localized Changes in Dentate Nucleus Shape and Magnetic Susceptibility in Friedreich Ataxia.

Author

Harding, Ian H., Nur Karim, Muhammad Ikhsan, Selvadurai, Louisa P., Corben, Louise A., Delatycki, Martin B., Monti, Serena, Saccà, Francesco, Georgiou‐Karistianis, Nellie, Cocozza, Sirio, and Egan, Gary F.

Abstract

Background: The dentate nuclei of the cerebellum are key sites of neuropathology in Friedreich ataxia (FRDA). Reduced dentate nucleus volume and increased mean magnetic susceptibility, a proxy of iron concentration, have been reported by magnetic resonance imaging studies in people with FRDA. Here, we investigate whether these changes are regionally heterogeneous. Methods: Quantitative susceptibility mapping data were acquired from 49 people with FRDA and 46 healthy controls. The dentate nuclei were manually segmented and analyzed using three dimensional vertex‐based shape modeling and voxel‐based assessments to identify regional changes in morphometry and susceptibility, respectively. Results: Individuals with FRDA, relative to healthy controls, showed significant bilateral surface contraction most strongly at the rostral and caudal boundaries of the dentate nuclei. The magnitude of this surface contraction correlated with disease duration, and to a lesser extent, ataxia severity. Significantly greater susceptibility was also evident in the FRDA cohort relative to controls, but was instead localized to bilateral dorsomedial areas, and also correlated with disease duration and ataxia severity. Conclusions: Changes in the structure of the dentate nuclei in FRDA are not spatially uniform. Atrophy is greatest in areas with high gray matter density, whereas increases in susceptibility—reflecting iron concentration, demyelination, and/or gliosis—predominate in the medial white matter. These findings converge with established histological reports and indicate that regional measures of dentate nucleus substructure are more sensitive measures of disease expression than full‐structure averages. Biomarker development and therapeutic strategies that directly target the dentate nuclei, such as gene therapies, may be optimized by targeting these areas of maximal pathology. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Published
2024
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20. An Ensemble of 2D Convolutional Neural Network for 3D Brain Tumor Segmentation

Author

Pawar, Kamlesh, Chen, Zhaolin, Jon Shah, N., Egan, Gary F., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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27. Improved Quantification of Cerebral Vein Oxygenation Using Partial Volume Correction

Author

Ward, Phillip GD, Fan, Audrey P, Raniga, Parnesh, Barnes, David G, Dowe, David L, Ng, Amanda CL, and Egan, Gary F

Subjects
Biomedical and Clinical Sciences, Neurosciences, Bioengineering, partial volume, vein, oxygen extraction fraction, OEF, quantitative susceptibility mapping, QSM, MRI, Psychology, Cognitive Sciences, Biological psychology
Abstract

Purpose: Quantitative susceptibility mapping (QSM) enables cerebral venous characterization and physiological measurements, such as oxygen extraction fraction (OEF). The exquisite sensitivity of QSM to deoxygenated blood makes it possible to image small veins; however partial volume effects must be addressed for accurate quantification. We present a new method, Iterative Cylindrical Fitting (ICF), to estimate voxel-based partial volume effects for susceptibility maps and use it to improve OEF quantification of small veins with diameters between 1.5 and 4 voxels. Materials and Methods: Simulated QSM maps were generated to assess the performance of the ICF method over a range of vein geometries with varying echo times and noise levels. The ICF method was also applied to in vivo human brain data to assess the feasibility and behavior of OEF measurements compared to the maximum intensity voxel (MIV) method. Results: Improved quantification of OEF measurements was achieved for vessels with contrast to noise greater than 3.0 and vein radii greater than 0.75 voxels. The ICF method produced improved quantitative accuracy of OEF measurement compared to the MIV approach (mean OEF error 7.7 vs. 12.4%). The ICF method provided estimates of vein radius (mean error

Published
2017

32. Multichannel Compressive Sensing MRI Using Noiselet Encoding

Author

Pawar, Kamlesh, Egan, Gary F., and Zhang, Jingxin

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract

The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI, and presents a method to design the pulse sequence for the noiselet encoding. This novel encoding scheme is combined with the multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. An empirical RIP analysis is presented to compare the multichannel noiselet and multichannel Fourier measurement matrices in MCS. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, two pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain.

Published
2014
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36. Correction to: A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility

Author

Abrams, Mathew Birdsall, Bjaalie, Jan G., Das, Samir, Egan, Gary F., Ghosh, Satrajit S., Goscinski, Wojtek J., Grethe, Jeffrey S., Kotaleski, Jeanette Hellgren, Ho, Eric Tatt Wei, Kennedy, David N., Lanyon, Linda J., Leergaard, Trygve B., Mayberg, Helen S., Milanesi, Luciano, Mouček, Roman, Poline, J. B., Roy, Prasun K., Strother, Stephen C., Tang, Tong Boon, Tiesinga, Paul, Wachtler, Thomas, Wójcik, Daniel K., and Martone, Maryann E.

Published
2022
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38. Free‐Water Imaging in Friedreich Ataxia Using Multi‐Compartment Models.

Author

Fernandez, Lara, Corben, Louise A., Bilal, Hiba, Delatycki, Martin B., Egan, Gary F., and Harding, Ian H.

Abstract

Background: The neurological phenotype of Friedreich ataxia (FRDA) is characterized by neurodegeneration and neuroinflammation in the cerebellum and brainstem. Novel neuroimaging approaches quantifying brain free‐water using diffusion magnetic resonance imaging (dMRI) are potentially more sensitive to these processes than standard imaging markers. Objectives: To quantify the extent of free‐water and microstructural change in FRDA‐relevant brain regions using neurite orientation dispersion and density imaging (NODDI), and bitensor diffusion tensor imaging (btDTI). Method: Multi‐shell dMRI was acquired from 14 individuals with FRDA and 14 controls. Free‐water measures from NODDI (FISO) and btDTI (FW) were compared between groups in the cerebellar cortex, dentate nuclei, cerebellar peduncles, and brainstem. The relative sensitivity of the free‐water measures to group differences was compared to microstructural measures of NODDI intracellular volume, free‐water corrected fractional anisotropy, and conventional uncorrected fractional anisotropy. Results: In individuals with FRDA, FW was elevated in the cerebellar cortex, peduncles (excluding middle), dentate, and brainstem (P < 0.005). FISO was elevated primarily in the cerebellar lobules (P < 0.001). On average, FW effect sizes were larger than all other markers (mean ηρ2 = 0.43), although microstructural measures also had very large effects in the superior and inferior cerebellar peduncles and brainstem (ηρ2 > 0.37). Across all regions and metrics, effect sizes were largest in the superior cerebellar peduncles (ηρ2 > 0.46). Conclusions: Multi‐compartment diffusion measures of free‐water and neurite integrity distinguish FRDA from controls with large effects. Free‐water magnitude in the brainstem and cerebellum provided the greatest distinction between groups. This study supports further applications of multi‐compartment diffusion modeling, and investigations of free‐water as a measure of disease expression and progression in FRDA. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function

Author

Wrigglesworth, Jo, primary, Ryan, Joanne, additional, Ward, Phillip G. D., additional, Woods, Robyn L., additional, Storey, Elsdon, additional, Egan, Gary F., additional, Murray, Anne, additional, Espinoza, Sara E., additional, Shah, Raj C., additional, Trevaks, Ruth E., additional, Ward, Stephanie A., additional, and Harding, Ian H., additional

Published
2023
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50. The Relationship between Long-Term Blood Pressure Variability and Cortical Thickness in Older Adults

Author

Gutteridge, Daria, primary, Segal, Ashlea, additional, McNeil, John J., additional, Beilin, Lawrence, additional, Brodtmann, Amy, additional, Chowdhury, Enayet, additional, Egan, Gary F., additional, Ernst, Michael E., additional, Hussain, Monira, additional, Reid, Christopher M., additional, Robb, Catherine, additional, Ryan, Joanne, additional, Woods, Robyn, additional, Keage, Hannah, additional, and Jamadar, Sharna, additional

Published
2023
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