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1. Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients

Author

Eloy Roura, Grégory Maclair, Magí Andorrà, Ferran Juanals, Irene Pulido-Valdeolivas, Albert Saiz, Yolanda Blanco, Maria Sepulveda, Sara Llufriu, Eloy Martínez-Heras, Elisabeth Solana, Elena H Martinez-Lapiscina, and Pablo Villoslada

Subjects
Multiple Sclerosis, Disability progression, Clinical outcomes, Brain MRI, Fractal dimension, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Background: Fractal geometry measures the morphology of the brain and detects CNS damage. We aimed to assess the longitudinal changes on brain’s fractal geometry and its predictive value for disease worsening in patients with Multiple Sclerosis (MS). Methods: We prospectively analyzed 146 consecutive patients with relapsing-remitting MS with up to 5 years of clinical and brain MRI (3 T) assessments. The fractal dimension and lacunarity were calculated for brain regions using box-counting methods. Longitudinal changes were analyzed in mixed-effect models and the risk of disability accumulation were assessed using Cox Proportional Hazard regression analysis. Results: There was a significant decrease in the fractal dimension and increases of lacunarity in different brain regions over the 5-year follow-up. Lower cortical fractal dimension increased the risk of disability accumulation for the Expanded Disability Status Scale [HR 0.9734, CI 0.8420–0.9125; Harrell C 0.59; Wald p 0.038], 9-hole peg test [HR 0.9734, CI 0.8420–0.9125; Harrell C 0.59; Wald p 0.0083], 2.5% low contrast vision [HR 0.4311, CI 0.2035–0.9133; Harrell C 0.58; Wald p 0.0403], symbol digit modality test [HR 2.215, CI 1.043–4.705; Harrell C 0.65; Wald p 0.0384] and MS Functional Composite-4 [HR 0.55, CI 0.317–0.955; Harrell C 0.59; Wald p 0.0029]. Conclusions: Fractal geometry analysis of brain MRI identified patients at risk of increasing their disability in the next five years.

Published
2021
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2. Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

Author

Sergi Valverde, Arnau Oliver, Eloy Roura, Deborah Pareto, Joan C. Vilanova, Lluís Ramió-Torrentà, Jaume Sastre-Garriga, Xavier Montalban, Àlex Rovira, and Xavier Lladó

Subjects
Brain, Multiple sclerosis, MRI, Brain atrophy, Automated tissue segmentation, White matter lesions, Lesion filling, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.

Published
2015
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3. Automated detection of Lupus white matter lesions in MRI

Author

Eloy Roura Perez, Nicolae Sarbu, Arnau Oliver, Sergi Valverde, Sandra González-Villà, Ricard Cervera, Núria Bargalló, and Xavier Lladó

Subjects
image analysis, Magnetic resonance images, Automatic lesion detection, Lupus disease, Automatic lesion segmentation, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative and quantitative results in terms of precision and sensitivity of lesion detection (True Positive Rate (62%) and Positive Prediction Value (80%) respectively) as well as segmentation accuracy (Dice Similarity Coefficient (72%)). Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration.

Published
2016
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14. Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury

Author

Eloy Roura, Saurabh Jain, Thijs Vande Vyvere, Andrew I R Maas, Diana M. Sima, Vasilis Terzopoulos, Guido Wilms, and Jan Verheyden

Subjects
030506 rehabilitation, medicine.medical_specialty, PREDICTION, Traumatic brain injury, Clinical Neurology, SEGMENTATION, Neuroimaging, Computed tomography, GUIDELINES, CLASSIFICATION, Head trauma, 03 medical and health sciences, SCANS, 0302 clinical medicine, Critical Care Medicine, General & Internal Medicine, Brain Injuries, Traumatic, Image Interpretation, Computer-Assisted, MANAGEMENT, medicine, Humans, Science & Technology, medicine.diagnostic_test, business.industry, traumatic brain injury, Neurosciences, HEAD-INJURY, deep learning, food and beverages, computed tomography, Original Articles, medicine.disease, MIDLINE SHIFT, quantification, 3. Good health, VOLUME, Neurosciences & Neurology, Neural Networks, Computer, Human medicine, Neurology (clinical), Radiology, Tomography, X-Ray Computed, 0305 other medical science, business, Life Sciences & Biomedicine, human activities, 030217 neurology & neurosurgery, CT
Abstract

Traumatic brain injury is a complex and diverse medical condition with a high frequency of intracranial abnormalities. These can typically be visualized on a computed tomography (CT) scan, which provides important information for further patient management, such as the need for operative intervention. In order to quantify the extent of acute intracranial lesions and associated secondary injuries, such as midline shift and cisternal compression, visual assessment of CT images has limitations, including observer variability and lack of quantitative interpretation. Automated image analysis can quantify the extent of intracranial abnormalities and provide added value in routine clinical practice. In this article, we present icobrain, a fully automated method that reliably computes acute intracranial lesions volume based on deep learning, cistern volume, and midline shift on the noncontrast CT image of a patient. The accuracy of our method is evaluated on a subset of the multi-center data set from the CENTER-TBI (Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury) study for which expert annotations were used as a reference. Median volume differences between expert assessments and icobrain are 0.07 mL for acute intracranial lesions and -0.01 mL for cistern segmentation. Correlation between expert assessments and icobrain is 0.91 for volume of acute intracranial lesions and 0.94 for volume of the cisterns. For midline shift computations, median error is -0.22 mm, with a correlation of 0.93 with expert assessments. ispartof: JOURNAL OF NEUROTRAUMA vol:36 issue:11 pages:1794-1803 ispartof: location:United States status: published

Published
2019
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15. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Author

Franca Wagner, Sorina Camarasu-Pop, Hélène Urien, Isabelle Bloch, Xavier Tomas-Fernandez, Jeremy Beaumont, John Muschelli, Chunliang Wang, Baptiste Laurent, Charles R.G. Guttmann, Tristan Glatard, Michael Kain, Mathieu Simon, Michel Dojat, April Khademi, Martin Styner, Pascal Girard, Thomas Tourdias, Michel M. dos Santos, Wellington Pinheiro dos Santos, Sergi Valverde, Richard McKinley, Abel G. Silva-Filho, Jesse Knight, Eloy Roura, Sandra Vukusic, Francisco Javier Vera-Olmos, François Cotton, Xavier Lladó, Elizabeth M. Sweeney, Senan Doyle, Anne Kerbrat, Olivier Commowick, Mariano Cabezas, Florent Leray, Amirreza Mahbod, Norberto Malpica, Gilles Edan, Roxana Ameli, Christian Barillot, Frederic Cervenansky, Florence Forbes, Simon K. Warfield, Audrey Istace, Jean-Christophe Ferré, Vision, Action et Gestion d'informations en Santé ( VisAGeS ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE ( IRISA_D5 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Centre Hospitalier Lyon Sud [CHU - HCL] ( CHLS ), Hospices Civils de Lyon ( HCL ), Laboratoire de Traitement de l'Information Medicale ( LaTIM ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest ( CHRU Brest ) -Université de Brest ( UBO ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Mines-Télécom [Paris], Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Service de neurochirurgie [Rennes], Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Service de neurologie [Rennes], Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ), Neuroinflammation: imagerie et thérapie de la sclérose en plaques, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Concordia University [Montreal], Pixyl Medical [Grenoble], Modelling and Inference of Complex and Structured Stochastic Systems ( MISTIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire Jean Kuntzmann ( LJK ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), University of Guelph, Department of Electrical and Computer Engineering [TORONTO] ( ECE ), University of Toronto, Royal Institute of Technology [Stockholm] ( KTH ), University of Bern, Johns Hopkins Bloomberg School of Public Health [Baltimore, MD, USA], Visio per computador I robotica ( VICOROB ), Universitat de Girona ( UdG ), Universidade Federal de Pernambuco [Recife] ( UFPE ), Computational Radiology Laboratory [Boston] ( CRL ), Brigham and Women's Hospital [Boston]-Children's Hospital, Laboratoire Traitement et Communication de l'Information ( LTCI ), Télécom ParisTech-Institut Mines-Télécom [Paris]-Université Paris-Saclay, Universidad Rey Juan Carlos [Madrid] ( URJC ), Brigham and Women's Hospital [Boston], Service de Neurologie, CHU Pontchaillou, Rennes, Grenoble Institut des Neurosciences ( GIN ), Université Joseph Fourier - Grenoble 1 ( UJF ) -CHU Grenoble-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Computer Science ( North Carolina State University ), North Carolina State University [Raleigh] ( NCSU ), Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Département de Radiologie [CHU de Rennes], Université de Rennes (UR), CHU Pontchaillou [Rennes], CHU Bordeaux [Bordeaux], Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Ryerson University [Toronto], Royal Institute of Technology [Stockholm] (KTH ), Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU), Research institute of Computer Vision and Robotics [Girona] (VICOROB), Universitat de Girona (UdG), Universidade Federal de Pernambuco [Recife] (UFPE), Computational Radiology Laboratory [Boston] (CRL), Brigham and Women's Hospital [Boston]-Boston Children's Hospital, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Universidad Rey Juan Carlos [Madrid] (URJC), Service de Neurologie [Lyon], CHU Lyon, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French 'Investissements d’Avenir' program) for funding and sponsoring the challenge. This work has also been partly supported by a grant (OFSEP) provided by the French State and handled by the 'Agence nationale de la recherche', within the framework of the 'Investissements d’Avenir' program, under the reference ANR-10-COHO-002. We also thank the French national cohort OFSEP (a French 'Investissements d’Avenir' program), and particularly the imaging group inside this cohort consortium for their constant support, fruitful discussions on the challenge and providing the MR images., ANR-11-INBS-0006,FLI,France Life Imaging(2011), ANR-10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Boston Children's Hospital-Brigham and Women's Hospital [Boston], Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-11-INBS-0006/11-INBS-0006,FLI,France In vivo Imaging(2011), ANR-10-COHO-002-01/10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Images et Modèles, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Service Informatique et développements, Department of Electrical and Computer Engineering [University of Toronto] (ECE), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Service de Neurologie [CHU Rennes], Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM), Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), RMN et optique : De la mesure au biomarqueur, Edan, Gilles, Infrastructures - France Life Imaging - - FLI2011 - ANR-11-INBS-0006 - INBS - VALID, Cohortes - Observatoire Français de la Sclérose en Plaques - - OFSEP2010 - ANR-10-COHO-0002 - COHO - VALID, and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, Computer science, Data management, MESH: Parenchymal Tissue, lcsh:Medicine, Esclerosi múltiple, computer.software_genre, 030218 nuclear medicine & medical imaging, MESH: Magnetic Resonance Imaging, Machine Learning, 0302 clinical medicine, open science, Image Processing, Computer-Assisted, Segmentation, [ SDV.IB ] Life Sciences [q-bio]/Bioengineering, Image processing -- Digital techniques, lcsh:Science, 610 Medicine & health, Multiple sclerosis lesion, image segmentation, MESH: Machine Learning, Magnetic Resonance Imaging, MESH: Image Processing, Computer-Assisted, Random forest, performance evaluation, MESH: Neural Networks, Computer, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Objective evaluation, Algorithms, MESH: Algorithms, Machine learning, Article, Multiple sclerosis, 03 medical and health sciences, Imatges -- Processament -- Tècniques digitals, distributed computing, Image Interpretation, Computer-Assisted, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Parenchymal Tissue, Retrospective Studies, MESH: Humans, business.industry, Deep learning, Imatge -- Segmentació, lcsh:R, MESH: Retrospective Studies, MESH: Multiple Sclerosis, MESH: Male, Imaging segmentation, computing infrastructure, [ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], lcsh:Q, Neural Networks, Computer, Artificial intelligence, business, computer, MESH: Female, MESH: Image Interpretation, Computer-Assisted, 030217 neurology & neurosurgery
Abstract

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French “Investissements d’Avenir” program) for funding and sponsoring the challenge. This work has also been partly supported by a grant (OFSEP) provided by the French State and handled by the “Agence nationale de la recherche”, within the framework of the “Investissements d’Avenir” program, under the reference ANR-10-COHO-002. We also thank the French national cohort OFSEP (a French “Investissements d’Avenir” program), and particularly the imaging group inside this cohort consortium for their constant support, fruitful discussions on the challenge and providing the MR images

Published
2018
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16. Advanced MRI techniques: biomarkers in neuropsychiatric lupus

Author

Gerard Espinosa, Mihaela Ionela Sarbu, Núria Bargalló, Eloy Roura, Anna Calvo, Pilar Toledano, Xavier Lladó, Nicolae Sarbu, and Ricard Cervera

Subjects
Adult, Male, Pathology, medicine.medical_specialty, Corpus Callosum, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Atrophy, Rheumatology, medicine, Humans, Lupus Erythematosus, Systemic, Prospective Studies, Gray Matter, Aged, 030203 arthritis & rheumatology, Mri techniques, Systemic lupus erythematosus, medicine.diagnostic_test, business.industry, Lupus Vasculitis, Central Nervous System, Magnetic resonance imaging, Middle Aged, medicine.disease, Prognosis, Magnetic Resonance Imaging, White Matter, Neuropsychiatric systemic lupus erythematosus, Diffusion Tensor Imaging, Female, business, 030217 neurology & neurosurgery
Abstract

Objectives The objective of this study was to determine whether advanced MRI could provide biomarkers for diagnosis and prognosis in neuropsychiatric systemic lupus erythematosus (NPSLE). Methods Our prospective study included 28 systemic lupus erythematosus (SLE) patients with primary central NPSLE, 22 patients without NPSLE and 20 healthy controls. We used visual scales to evaluate atrophy and white matter hyperintensities, voxel-based morphometry and Freesurfer to measure brain volume, plus diffusion-tensor imaging (DTI) to assess white matter (WM) and gray matter (GM) damage. We compared the groups and correlated MRI abnormalities with clinical data. Results NPSLE patients had less GM and WM than controls ( p = 0.042) in the fronto-temporal regions and corpus callosum. They also had increased diffusivities in the temporal lobe WM ( p Conclusions Atrophy and microstructural damage in fronto-temporal WM and GM in NPSLE correlate with severity, activity and the time from disease onset. Antimalarial treatment seems to give some brain-protective effects.

Published
2017

17. Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations

Author

Xavier Lladó, Sergi Valverde, Eloy Roura, Arnau Oliver, and Mariano Cabezas

Subjects
Tissue segmentation, Computer science, business.industry, Pattern recognition, Brain tissue, computer.software_genre, Sørensen–Dice coefficient, WHITE MATTER TISSUE, Voxel, Brain segmentation, Radiology, Nuclear Medicine and imaging, Segmentation, Pairwise comparison, Artificial intelligence, business, computer
Abstract

Purpose Ground-truth annotations from the well-known Internet Brain Segmentation Repository (IBSR) datasets consider Sulcal cerebrospinal fluid (SCSF) voxels as gray matter. This can lead to bias when evaluating the performance of tissue segmentation methods. In this work we compare the accuracy of 10 brain tissue segmentation methods analyzing the effects of SCSF ground-truth voxels on accuracy estimations. Materials and Methods The set of methods is composed by FAST, SPM5, SPM8, GAMIXTURE, ANN, FCM, KNN, SVPASEG, FANTASM, and PVC. Methods are evaluated using original IBSR ground-truth and ranked by means of their performance on pairwise comparisons using permutation tests. Afterward, the evaluation is repeated using IBSR ground-truth without considering SCSF. Results The Dice coefficient of all methods is affected by changes in SCSF annotations, especially on SPM5, SPM8 and FAST. When not considering SCSF voxels, SVPASEG (0.90 ± 0.01) and SPM8 (0.91 ± 0.01) are the methods from our study that appear more suitable for gray matter tissue segmentation, while FAST (0.89 ± 0.02) is the best tool for segmenting white matter tissue. Conclusion The performance and the accuracy of methods on IBSR images vary notably when not considering SCSF voxels. The fact that three of the most common methods (FAST, SPM5, and SPM8) report an important change in their accuracy suggest to consider these differences in labeling for new comparative studies. J. Magn. Reson. Imaging 2014. © 2014 Wiley Periodicals, Inc. J. Magn. Reson. Imaging 2015;41:93–101. © 2014 Wiley Periodicals, Inc.

Published
2014
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18. Automated tissue segmentation of MR brain images in the presence of white matter lesions

Author

Xavier Lladó, Sandra González-Villà, Lluís Ramió-Torrentà, Sergi Valverde, Deborah Pareto, Joan C. Vilanova, Arnau Oliver, Alex Rovira, Eloy Roura, and Ministerio de Economía y Competitividad (Espanya)

Subjects
Multiple Sclerosis, Partial volume, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Health Informatics, Fluid-attenuated inversion recovery, 030218 nuclear medicine & medical imaging, Automation, 03 medical and health sciences, Imatges -- Processament -- Tècniques digitals, 0302 clinical medicine, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Computer vision, Segmentation, Image processing -- Digital techniques, Multiple sclerosis -- Imaging, Multiple sclerosis -- Magnetic resonance imaging, Radiological and Ultrasound Technology, Tissue segmentation, business.industry, Esclerosi múltiple -- Imatges per ressonància magnètica, Probabilistic logic, Brain, Magnetic Resonance Imaging, White Matter, Computer Graphics and Computer-Aided Design, Pipeline (software), Hyperintensity, Imatges -- Segmentació, Esclerosi múltiple -- Imatges, Imaging segmentation, Outlier, Imatgeria mèdica, Computer Vision and Pattern Recognition, Artificial intelligence, business, Algorithms, 030217 neurology & neurosurgery, Imaging systems in medicine
Abstract

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community This work has been partially supported by ”La Fundacióla Maratóde TV3”, by Retos de Investigacin TIN2014-55710-R, and by the MPC UdG 2016/022 grant from the University of Girona

Published
2017

19. A toolbox for multiple sclerosis lesion segmentation

Author

Arnau Oliver, Eloy Roura, Mariano Cabezas, Joan C. Vilanova, Alex Rovira, Xavier Lladó, Sergi Valverde, Lluís Ramió-Torrentà, Deborah Pareto, and Ministerio de Economía y Competitividad (Espanya)

Subjects
medicine.medical_specialty, Multiple Sclerosis, Software Validation, Esclerosi múltiple, Fluid-attenuated inversion recovery, Sensitivity and Specificity, Pattern Recognition, Automated, White matter, Machine Learning, Multiple sclerosis, Imatges -- Processament -- Tècniques digitals, Image Interpretation, Computer-Assisted, medicine, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Image processing -- Digital techniques, Neuroradiology, Lesion segmentation, medicine.diagnostic_test, business.industry, Imatge -- Segmentació, Reproducibility of Results, Pattern recognition, Magnetic resonance imaging, medicine.disease, Image Enhancement, Magnetic Resonance Imaging, Toolbox, medicine.anatomical_structure, Imaging segmentation, Neurology (clinical), Radiology, Artificial intelligence, Cardiology and Cardiovascular Medicine, business, Algorithms, Software
Abstract

Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images. Methods: Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image. Results: The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches. Conclusion: Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities E. Roura holds a BRUdG2013 grant. S. Valverde holds a FI-DGR2013 grant from the Generalitat de Catalunya. This work has been partially supported by BLa Fundació la Marató de TV3^ and by Retos de Investigación TIN2014-55710-R This work has been partially supported by BLa Fundació la Marató de TV3^ and by Retos de Investigación TIN2014-55710-R

Published
2015

20. Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations

Author

Sergi, Valverde, Arnau, Oliver, Mariano, Cabezas, Eloy, Roura, and Xavier, Lladó

Subjects
Brain Mapping, Image Interpretation, Computer-Assisted, Brain, Datasets as Topic, Humans, Reproducibility of Results, Magnetic Resonance Imaging, Algorithms, Pattern Recognition, Automated
Abstract

Ground-truth annotations from the well-known Internet Brain Segmentation Repository (IBSR) datasets consider Sulcal cerebrospinal fluid (SCSF) voxels as gray matter. This can lead to bias when evaluating the performance of tissue segmentation methods. In this work we compare the accuracy of 10 brain tissue segmentation methods analyzing the effects of SCSF ground-truth voxels on accuracy estimations.The set of methods is composed by FAST, SPM5, SPM8, GAMIXTURE, ANN, FCM, KNN, SVPASEG, FANTASM, and PVC. Methods are evaluated using original IBSR ground-truth and ranked by means of their performance on pairwise comparisons using permutation tests. Afterward, the evaluation is repeated using IBSR ground-truth without considering SCSF.The Dice coefficient of all methods is affected by changes in SCSF annotations, especially on SPM5, SPM8 and FAST. When not considering SCSF voxels, SVPASEG (0.90 ± 0.01) and SPM8 (0.91 ± 0.01) are the methods from our study that appear more suitable for gray matter tissue segmentation, while FAST (0.89 ± 0.02) is the best tool for segmenting white matter tissue.The performance and the accuracy of methods on IBSR images vary notably when not considering SCSF voxels. The fact that three of the most common methods (FAST, SPM5, and SPM8) report an important change in their accuracy suggest to consider these differences in labeling for new comparative studies.

Published
2013

21. FRI0311 Neuropsychiatric Lupus: Combining Advanced Morphometric Methods, Diffusion Tensor Imaging and Conventional MRI Leads To The Pathways of Brain Abnormalities. Correlation with Clinical and Laboratory Data

Author

Nicolae Sarbu, Pilar Toledano, Gerard Espinosa, X. Lladό, Eloy Roura, Ricard Cervera, N. Bargallό, Anna Calvo, and Mihaela Ionela Sarbu

Subjects
Temporal cortex, Pathology, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Immunology, Magnetic resonance imaging, Hippocampal formation, medicine.disease, computer.software_genre, Corpus callosum, General Biochemistry, Genetics and Molecular Biology, White matter, Atrophy, medicine.anatomical_structure, Rheumatology, Voxel, medicine, Immunology and Allergy, Nuclear medicine, business, computer, Diffusion MRI
Abstract

Objectives To combine conventional and advanced magnetic resonance imaging (MRI) to describe brain abnormalities in neuropsychiatric (NP) systemic lupus erythematosus (SLE), and to correlate them with clinical data. Methods Prospective study including patients presenting central primary NPSLE, non-NPSLE and healthy controls (HC) undergoing brain MRI at 3T in 2014–2015. Conventional MRI data using visual scales for atrophy and white matter (WM) lesions, brain volumetry (voxel-based morphometry and Freesurfer), and diffusion-tensor imaging (DTI) were analyzed. Group differences were correlated with clinical, laboratory and treatment data. Results Twenty-eight females with NPSLE, 22 non-NPSLE and 20 HC, mean age around 40 years, were recruited. Conventional MRI showed brain atrophy in NPSLE (p=0.005). Total cortical volume was reduced in NPSLE compared to HC (p=0,042). Non-NPSLE had a reduction of left temporal gray-matter (GM) volume compared to HC (p=0.028). NPSLE had a decrease of left frontal WM volume compared with HC (p=0.002). Corpus callosum was reduced in NPSLE compared to HC (p=0.033) and non-NPSLE (p=0.043). DTI showed increased diffusivity (mean, axial and radial: MD, AD, RD) in bilateral temporal WM (p SLEDAI score was correlated with atrophy (p=0.046); negatively with left temporal cortex volume (p=0.029) and with FA in WM hippocampal regions (p=0.002 left, p=0.048 right). SLICC score was correlated with diffusivity in bilateral temporal WM (MD:p=0.033; AD:p=0.039; RD:p=0.016) and right frontal WM (p=0.005); and negatively with FA in left WM hippocampal regions (p Antimalarials were negatively correlated with atrophy on conventional MRI (p=0.004), total GM volume (p=0.021) and subcortical GM volume (p=0.020); specifically with right and left frontal cortex (p=0.029 and 0.015) and right and left thalamus (p=0.009 and 0.047). Antimalarials were correlated with lower diffusivity in multiple WM clusters (right and left temporal: decreased MD, p=0.006 and 0.004; AD, p=0.007 bilaterally; RD, p=0.005 bilaterally; and right frontal: decreased RD, p=0.010). Conclusions Atrophy and DTI changes indicating microstructural damage in frontal and temporal white and gray matter are the most important radiological findings in NPSLE. Assessing them should become the milestone of monitoring this disease. This changes correlates with the severity, activity and duration of the disease. Antimalarial treatment could produce some protective effect on the brain in SLE patients. Disclosure of Interest None declared

Published
2016
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22. Agents for Social Search in Long-Term Digital Preservation

Author

Kuldar Aas, Raivo Ruusalepp, Josep Lluis de la Rosa, Eloy Roura, Alex Moreno, Teresa Bosch, Albert Trias, and Albert Bres

Subjects
World Wide Web, Social search, Risk analysis (engineering), Digital preservation, Computer science, Multi-agent system, Question answering, Context (language use), Information exchange, Term (time), Ranking (information retrieval)
Abstract

This paper describes the application of agents to automate information exchange for digital preservation. Agents are able to recommend preservation solutions and also apply them to different preservation situations. Trust models for question-routing and answer ranking that are implemented by means of agents, show greater performance than traditional keyword search methodologies.

Published
2010
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23. Multi-channel registration of fractional anisotropy and T1-weighted images in the presence of atrophy: Application to multiple sclerosis

Author

Sebastien Ourselin, Torben Schneider, Declan T. Chard, Marc Modat, Nils Muhlert, Xavier Lladó, Pankaj Daga, Claudia Gandini Wheeler-Kingshott, Eloy Roura, and Ministerio de Economía y Competitividad (Espanya)

Subjects
Adult, Male, Multiple Sclerosis, Computer science, Image processing, Esclerosi múltiple, Sensitivity and Specificity, Multiple sclerosis, Imatges -- Processament -- Tècniques digitals, Atrophy, Neuroimaging, Fractional anisotropy, Image Processing, Computer-Assisted, medicine, Humans, Brain segmentation, Image processing -- Digital techniques, Anisotropy, business.industry, General Neuroscience, Imatge -- Segmentació, Brain, Pattern recognition, Articles, General Medicine, Middle Aged, medicine.disease, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Imaging segmentation, Female, Neurology (clinical), Artificial intelligence, Nuclear medicine, business, Algorithms, Diffusion MRI
Abstract

Co-registration of structural T1-weighted (T1w) scans and diffusion tensor imaging (DTI)-derived fractional anisotropy (FA) maps to a common space is of particular interest in neuroimaging, as T1w scans can be used for brain segmentation while DTI can provide microstructural tissue information. While the effect of lesions on registration has been tackled and solutions are available, the issue of atrophy is still open to discussion. Multi-channel (MC) registration algorithms have the advantage of maintaining anatomical correspondence between different contrast images after registration to any target space. In this work, we test the performance of an MC registration approach applied to T1w and FA data using simulated brain atrophy images. Experimental results are compared with a standard single-channel registration approach. Multi-channel registration of fractional anisotropy and T1-weighted images in the presence of atrophy: application to multiple sclerosis Both qualitative and quantitative evaluations are presented, showing that the MC approach provides better alignment with the target while maintaining better T1w and FA co-alignment This work has also been partially supported by “La Fundació la Marató de TV3” and by Retos de Investigación TIN2014-55710-R

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