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18 results on '"Guttmann, C. R. G."'

1. Corrigendum: Perivascular Unit: This Must Be the Place. The Anatomical Crossroad Between the Immune, Vascular and Nervous System (Frontiers in Neuroanatomy, (2020), 14, 10.3389/fnana.2020.00017)

Author

Troili, F., Cipollini, V., Moci, M., Morena, E., Palotai, M., Rinaldi, V., Romano, C., Ristori, G., Giubilei, F., Salvetti, M., Orzi, F., Guttmann, C. R. G., and Cavallari, M.

Subjects
amyloid, aquaporin (AQP)-4, blood brain barrier (BBB), glymphatic system, neurodegenaration, neuroinflammation, perivascular space (PVS)
Published
2020

2. Evidence of axonal damage in cerebellar peduncles without T2-lesions in multiple sclerosis

Author

Salem Hannoun, Kocevar, G., Durand-Dubief, F., Stamile, C., Naji, A., Cotton, F., Cavallari, M., Guttmann, C. R. G., Sappey-Marinier, D., RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), 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 Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), American University of Beirut [Beyrouth] (AUB), Service de Neurologie à l'Hôpital neurologique de Lyon, Hôpital neurologique et neurochirurgical Pierre Wertheimer [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Département de Neuroradiologie [Centre Hospitalier Lyon Sud - HCL], Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Center for Neurological Imaging, Departments of Radiology and Neurology, Centre d'Etude et de Recherche Multimodal Et Pluridisciplinaire en imagerie du vivant (CERMEP - imagerie du vivant), Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-CHU Grenoble-Hospices Civils de Lyon (HCL)-CHU Saint-Etienne-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 Grenoble Alpes (UGA)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

3. A distributed platform for making large scale manual reference datasets for MS lesion segmentation

Author

Damangir, S., de Sitter, A., Brouwer, I., Guttmann, C. R. G., Pareto, Deborah, Rovira, Alex, Barkhof, F., Vrenken, H., Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Neuroinfection & -inflammation, and CCA - Cancer biology and immunology

Published
2018

4. Creating accurate reference segmentations of deep GM structures in MS patients by fast semi-automated outlining

Author

Sitter, A., Bartel, F., Palotai, M., Burggraaff, J., Liu, Y., Simoes, J., Ruggieri, S., Schregel, K., Pinzon, A. Morales, Ropele, S., Rocca, M. A., Gasperini, C., Gallo, A., Schoonheimm, M., Amann, M., Yiannakas, M., Wattjes, M. P., Sastre-Garriga, J., Kappos, L., Massimo Filippi, Enzinger, C., Ciccarelli, O., Frederiksen, J., Barkhof, F., Guttmann, C. R. G., Munck, J. C., Vrenken, H., Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Neurology, Medical psychology, Other Research, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Neuroinfection & -inflammation, CCA - Cancer biology and immunology, and Amsterdam Neuroscience - Neurovascular Disorders

Published
2018

5. New insight in perivenular lesion formation in multiple sclerosis on weekly susceptibility weighted images

Author

Mure, S., Guttmann, C. R. G., Grenier, T., Benoit-Cattin, H., Cotton, F., Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), 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 Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Center for Neurological Imaging, Departments of Radiology and Neurology, and RMN et optique : De la mesure au biomarqueur

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; In this paper, we take advantage of a unique longitudinal MRI dataset acquired at weekly intervals on untreated multiple sclerosis patients. We study the signal dynamics of relapsing-remitting multiple sclerosis lesions on SWI MRI and show, thanks to an unsupervised spatiotemporal clustering algorithm, that specific signal intensity behaviors exist between the veins and the lesions that are synchronous with contrast enhancement on gadolinium-enhanced T1-weighted MRI. Our study shows that vein narrowing depicted on SWI is an early event that appears to precede blood-brain barrier disruption signified by contrast-enhancement.

Published
2016

7. Lesion Effects on Cerebellar Peduncles DTI Metrics in MS Patients

Author

Salem Hannoun, Durand-Dubief, F., Ibarrola, D., Cavallari, C., Confavreux, C., Guttmann, C. R. G., Sappey Marinier, D., RMN et optique : De la mesure au biomarqueur, 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)-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), Centre d'Etude et de Recherche Multimodal Et Pluridisciplinaire en imagerie du vivant (CERMEP - imagerie du vivant), Université de Lyon-Université de Lyon-CHU Grenoble-Hospices Civils de Lyon (HCL)-CHU Saint-Etienne-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 Grenoble Alpes (UGA), Center for Neurological Imaging, and Departments of Radiology and Neurology

Subjects
[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2014

8. Lesions effects on cerebellar peduncles DTI metrics in MS patients

Author

Salem Hannoun, Sappey Marinier, D., Durand-Dubief, F., Ibarrola, D., Confavreux, C., Cavallari, C., Cotton, F., Guttmann, C. R. G., RMN et optique : De la mesure au biomarqueur, 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)-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), Centre d'Etude et de Recherche Multimodal Et Pluridisciplinaire en imagerie du vivant (CERMEP - imagerie du vivant), Université de Lyon-Université de Lyon-CHU Grenoble-Hospices Civils de Lyon (HCL)-CHU Saint-Etienne-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 Grenoble Alpes (UGA), Service de Radiologie, Hospices Civils de Lyon (HCL), Center for Neurological Imaging, and Departments of Radiology and Neurology

Subjects
[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2013

9. Long-Interval T2-Weighted Subtraction Magnetic Resonance Imaging A Powerful New Outcome Measure in Multiple Sclerosis Trials

Author

Moraal, B., Den Elskamp, I. J., Knol, D. L., Bernard Uitdehaag, Geurts, J. J. G., Vrenken, H., Pouwels, P. J. W., Schijndel, R. A., Meier, D. S., Guttmann, C. R. G., Barkhof, F., Radiology and nuclear medicine, Epidemiology and Data Science, Neurology, Pathology, Anatomy and neurosciences, Physics and medical technology, NCA - Multiple Sclerosis and Other Neuroinflammatory Diseases, NCA - Neurodegeneration, Neuroscience Campus Amsterdam - Neurodegeneration, and Neuroscience Campus Amsterdam - Multiple Sclerosis and Other Neuroinflammatory Diseases

Subjects
SDG 3 - Good Health and Well-being
Abstract

Objective: To compare long-interval T2-weighted subtraction (T2w-Sub) imaging with monthly gadolinium-enhanced. T1-weighted (Gd-T1w) imaging for (1) detection of active lesions, (2) assessment of treatment efficacy, and (3) statistical power, in a multiple sclerosis (MS), phase 2, clinical trial setting. Methods: Magnetic resonance imaging (MRI) data over 9 months from 120 patients (61 treatment, 59 placebo) from the oral temsirolimus trial were used. T2w-Sub images were scored for active lesions, independent of the original reading of the monthly Gd-T1w images. Treatment efficacy was evaluated using the nonparametric Mann-Whitney U test, and parametric negative binomial (NB)-regression and power calculations were conducted. Results: Datasets from 116 patients (58 treatment, 58 placebo) were evaluated. The mean number of T2w-Sub lesions in the treatment group was 3.0 (±4.6) versus 5.9 (±8.8) for placebo; the mean cumulative number of new Gd-T1w lesions in the treatment group was 5.5(±9.1) versus 9.1(±17.2) for placebo. T2w-Sub imaging showed increased power to assess treatment efficacy compared with Gd-T1w imaging, when evaluated by Mann-Whitney U test (p = 0.017 vs p = 0.177), or NB-regression without (p = 0.011 vs p = 0.092) or with baseline adjustment (p < 0.001 vs p = 0.002). Depending on the magnitude of the simulated treatment effect, sample size calculations showed reductions of 22 to 34% in the number of patients (translating into reductions of 81-83% in the number of MRI scans) needed to detect a significant treatment effect in favor of T2w-Sub imaging. Interpretation: Compared with monthly Gd-T1w imaging, long-interval T2w-Sub MRI exhibited increased power to assess treatment efficacy, and could greatly increase the cost-effectiveness of phase 2 MS trials by limiting the number of patients, contrast injections, and MRI scans needed. © 2010 American Neurological Association.

Published
2010

10. MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information

Author

Milles, J., Zhu, Y. M., Gimenez, G., Guttmann, C. R. G., Magnin, I. E., Centre de Recherche et d'Application en Traitement de l'Image et du Signal (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)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Laboratoire Creatis, Compte Général

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [SPI.MECA.MEFL] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], [SPI.MECA.MEMA] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of materials [physics.class-ph], [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], [PHYS.MECA.MEMA]Physics [physics]/Mechanics [physics]/Mechanics of materials [physics.class-ph], [PHYS.MECA.MEMA] Physics [physics]/Mechanics [physics]/Mechanics of materials [physics.class-ph], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.MECA.MEMA]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of materials [physics.class-ph], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [PHYS.MECA.MEFL] Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, [SDV.IB] Life Sciences [q-bio]/Bioengineering, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Mechanics of the fluids [physics.class-ph], [PHYS.MECA.BIOM] Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [PHYS.MECA.ACOU]Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [PHYS.MECA.ACOU] Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

article

Published
2007

11. Computation of Transmitted and Received B1 Fields in Magnetic Resonance Imaging

Author

Milles, J., Zhu, Y. M., Chen, N., Panych, L., Gimenez, G., Guttmann, C. R. G., Laboratoire Creatis, Compte Général, Centre de Recherche et d'Application en Traitement de l'Image et du Signal (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [SPI.MECA.MEFL] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], [SPI.MECA.MEMA] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of materials [physics.class-ph], [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], [PHYS.MECA.MEMA]Physics [physics]/Mechanics [physics]/Mechanics of materials [physics.class-ph], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [PHYS.MECA.MEMA] Physics [physics]/Mechanics [physics]/Mechanics of materials [physics.class-ph], [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.MECA.MEMA]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of materials [physics.class-ph], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [PHYS.MECA.MEFL] Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SDV.IB] Life Sciences [q-bio]/Bioengineering, [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Mechanics of the fluids [physics.class-ph], [PHYS.MECA.BIOM] Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [PHYS.MECA.ACOU]Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [PHYS.MECA.ACOU] Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

article

Published
2006

12. Multi-centre assessment of artificially generated MRI for cortical and juxtacortical multiple sclerosis lesion detection

Author

Bouman, P. M., Noteboom, S., Santos, F. A. Nobrega, Beck, E. S., Bliault, G., Marco Castellaro, Calabrese, M., Chard, D. T., Eichinger, P., Filippi, M., Inglese, M., Lapucci, C., Marciniak, A., Moraal, B., Pinzon, A. Moralez, Muehlau, M., Preziosa, P., Reich, D. S., Rocca, M. A., Schoonheim, M. M., Twisk, J. W., Wiestler, B., Jonkman, L. E., Guttmann, C. R. G., Geurts, J. J. G., and Steenwijk, M. D.

14. Sensitive Detection of Caudate and Thalamic Alterations in Multiple Sclerosis Patients by Diffusion Tensor Imaging

Author

Sappey Marinier, D., Salem Hannoun, Durand-Dubief, F., Ibarrola, D., Confavreux, C., Guttmann, C. R. G., RMN et optique : De la mesure au biomarqueur, 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)-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), Centre d'Etude et de Recherche Multimodal Et Pluridisciplinaire en imagerie du vivant (CERMEP - imagerie du vivant), Université de Lyon-Université de Lyon-CHU Grenoble-Hospices Civils de Lyon (HCL)-CHU Saint-Etienne-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 Grenoble Alpes (UGA), Center for Neurological Imaging, and Departments of Radiology and Neurology

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

15. Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods

Author

Jette L. Frederiksen, Mike P. Wattjes, Ana Rovira, Charles R.G. Guttmann, P. C. de Witt Hamer, I. Brouwer, R.A. van Schijndel, Hugo Vrenken, Marjolein Visser, Massimo Filippi, L Kappos, Marnix G. Witte, Olga Ciccarelli, Stefan Ropele, Frederik Barkhof, A. de Sitter, Keith S. Cover, Roelant S Eijgelaar, Alzheimer’s Disease Neuroimaging Initiative, Christian Enzinger, D. M. J. Müller, de Sitter, A, Visser, M, Brouwer, I, Cover, K S, van Schijndel, R A, Eijgelaar, R S, Müller, D M J, Ropele, S, Kappos, L, Rovira, Á, Filippi, M, Enzinger, C, Frederiksen, J, Ciccarelli, O, Guttmann, C R G, Wattjes, M P, Witte, M G, de Witt Hamer, P C, Barkhof, F, Vrenken, H, MAGNIMS Study Group and Alzheimer’s Disease Neuroimaging, Initiative, Rocca, M. A., Biophotonics and Medical Imaging, LaserLaB - Biophotonics and Microscopy, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, AGEM - Endocrinology, metabolism and nutrition, APH - Aging & Later Life, APH - Health Behaviors & Chronic Diseases, Neurosurgery, and Other Research

Subjects
Male, medicine.medical_specialty, Multiple Sclerosis, Outcome measurements, Neuroimaging, 030218 nuclear medicine & medical imaging, Database, 03 medical and health sciences, Magnetic resonance imaging, 0302 clinical medicine, Alzheimer Disease, Image Interpretation, Computer-Assisted, medicine, Humans, Radiology, Nuclear Medicine and imaging, Magnetic Resonance, Ethic, Analysis method, Aged, Neuroradiology, Aged, 80 and over, Ethics, Lesion segmentation, medicine.diagnostic_test, Information Dissemination, business.industry, Outcome measures, Brain, Reproducibility of Results, General Medicine, Middle Aged, medicine.disease, Tumor Burden, Privacy, Face, Female, Radiology, Glioblastoma, business, Algorithms, Confidentiality, 030217 neurology & neurosurgery
Abstract

Background Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants’ privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. Methods FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests. Results Automated analysis methods failed in 0–19% of cases in FFR-processed images versus 0–2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p Conclusions All three outcome measures were affected differently by FFR, including failure of analysis methods and both “random” variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants’ privacy. Key Points • Protecting participants’ privacy when sharing MRI data is important. • Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups. • Removing facial features degrades performance of image analysis methods.

Published
2019

16. Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references

Author

Massimo Filippi, Jessica Burggraaff, Jette L. Frederiksen, Claudio Gasperini, Jaume Sastre-Garriga, Frederik Barkhof, Marios C. Yiannakas, Christian Enzinger, Mike P. Wattjes, Menno M. Schoonheim, Bernard M. J. Uitdehaag, Maria A. Rocca, Antonio Gallo, Serena Ruggieri, Deborah Pareto, Hugo Vrenken, Yaou Liu, Ludwig Kappos, Alexandra de Sitter, Michael Amann, Miklos Palotai, Charles R.G. Guttmann, Stefan Ropele, Jorge Simoes, Fabian Bartel, Katharina Schregel, Radiology and nuclear medicine, Neurology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Other Research, de Sitter, A., Burggraaff, J., Bartel, F., Palotai, M., Liu, Y., Simoes, J., Ruggieri, S., Schregel, K., Ropele, S., Rocca, M. A., Gasperini, C., Gallo, A., Schoonheim, M. M., Amann, M., Yiannakas, M., Pareto, D., Wattjes, M. P., Sastre-Garriga, J., Kappos, L., Filippi, M., Enzinger, C., Frederiksen, J., Uitdehaag, B., Guttmann, C. R. G., Barkhof, F., and Vrenken, H.

Subjects
Atrophy, Deep grey matter, MRI, Multiple Sclerosis, Reference set, Segmentation, Jaccard index, Computer science, Cognitive Neuroscience, Computer applications to medicine. Medical informatics, R858-859.7, Reproducibility of Result, Grey matter, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Thalamus, Multiple Sclerosi, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Gray Matter, RC346-429, Thalamu, Reliability (statistics), Protocol (science), Reproducibility, business.industry, Multiple sclerosis, 05 social sciences, Reproducibility of Results, Regular Article, Pattern recognition, medicine.disease, Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, Manual segmentation, Neurology. Diseases of the nervous system, Neurology (clinical), Artificial intelligence, business, 030217 neurology & neurosurgery, Human
Abstract

Background Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. Objectives This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). Methods A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. Results All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. Conclusions The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.

Published
2021

17. Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Author

Burggraaff, Jessica, Liu, Yao, Prieto, Juan C., Simoes, Jorge, de Sitter, Alexandra, Ruggieri, Serena, Brouwer, Iman, Lissenberg-Witte, Birgit I., Rocca, Mara A., Valsasina, Paola, Ropele, Stefan, Gasperini, Claudio, Gallo, Antonio, Pareto, Deborah, Sastre-Garriga, Jaume, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Ciccarelli, Olga, Hulst, Hanneke E., Wattjes, Mike P., Barkhof, Frederik, Uitdehaag, Bernard M. J., Vrenken, Hugo, Guttmann, Charles R. G., Universitat Autònoma de Barcelona, Neurology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Epidemiology and Data Science, Anatomy and neurosciences, Other Research, APH - Methodology, UCL - (SLuc) Centre du cancer, UCL - (SLuc) Service de chirurgie et transplantation abdominale, UCL - SSS/IREC/CHEX - Pôle de chirgurgie expérimentale et transplantation, Institut Català de la Salut, [Burggraaff J, Simoes J] Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands. [Liu Y, de Sitter A] Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands. [Prieto JC] Center for Neurological Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, USA. [Ruggieri S] Department of Human Neurosciences, 'Sapienza' University of Rome, Piazzale Aldo Moro, 5, 00185 Roma RM, Italy. Department of Neurosciences, San Camillo Forlanini Hospital, Circonvallazione Gianicolense, 87, 00152 Roma RM, Italy. [Pareto D] Secció de Neuroradiologia, Unitat de Ressonància Magnètica, Departament de Radiologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. [Sastre-Garriga J] Servei de Neurologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain, Vall d'Hebron Barcelona Hospital Campus, Burggraaff, J., Liu, Y., Prieto, J. C., Simoes, J., de Sitter, A., Ruggieri, S., Brouwer, I., Lissenberg-Witte, B. I., Rocca, M. A., Valsasina, P., Ropele, S., Gasperini, C., Gallo, A., Pareto, D., Sastre-Garriga, J., Enzinger, C., Filippi, M., De Stefano, N., Ciccarelli, O., Hulst, H. E., Wattjes, M. P., Barkhof, F., Uitdehaag, B. M. J., Vrenken, H., and Guttmann, C. R. G.

Subjects
WLG, Word List Generation, SPM12, Statistical Parametric Mapping 12, Audiology, Tàlem - Imatgeria, Etiv, estimated total intracranial volume, lcsh:RC346-429, Otros calificadores::Otros calificadores::/complicaciones [Otros calificadores], GIF, Geodesic Information Flows, 0302 clinical medicine, Cognition, Segmentation, Thalamus, CP, cognitively preserved, NBV, Normalized brain volume, Multiple Sclerosi, Other subheadings::/diagnosis [Other subheadings], Neuropsychological assessment, Cognitive decline, Generalized estimating equation, WMV, white matter volume, ICC, intraclass correlation coefficient, medicine.diagnostic_test, 05 social sciences, Nervous System Diseases::Autoimmune Diseases of the Nervous System::Demyelinating Autoimmune Diseases, CNS::Multiple Sclerosis [DISEASES], sistema nervioso::sistema nervioso central::encéfalo::prosencéfalo::diencéfalo::tálamo [ANATOMÍA], Regular Article, Neuropsychological test, HC, healthy control, SDMT, Symbol Digit Modalities Test, Magnetic Resonance Imaging, Neurology, PASAT, Paced Auditory Serial Addition Test, enfermedades del sistema nervioso::enfermedades autoinmunitarias del sistema nervioso::enfermedades autoinmunes desmielinizantes del SNC::esclerosis múltiple [ENFERMEDADES], lcsh:R858-859.7, VolBrain, MRI Brain Volumetry System, SRT, Selective Reminding Test, Human, MRI, Esclerosi múltiple - Complicacions, medicine.medical_specialty, CNR, contrast-to-noise ratio, Multiple Sclerosis, Cognitive Neuroscience, RRMS, Relapsing-Remitting Multiple Sclerosis, Otros calificadores::/diagnóstico [Otros calificadores], Nervous System::Central Nervous System::Brain::Prosencephalon::Diencephalon::Thalamus [ANATOMY], 10/36 SRT, 10/36 Spatial Recall Test, lcsh:Computer applications to medicine. Medical informatics, NGMV, Normalized grey matter volume, 050105 experimental psychology, SD, standard deviations, CII, cognitive impairment index, EDSS, Expanded Disability Status Scale, WCST, Wisconsin Card Sorting Test, 03 medical and health sciences, Atrophy, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, WM, white matter, lcsh:Neurology. Diseases of the nervous system, Thalamu, CI, cognitively impaired and preserved (CP), BRB-N, Brief Repeatable Battery of Neuropsychological Tests, business.industry, Multiple sclerosis, FSL-FIRST, FMRIB Integrated Registration and Segmentation Tool, eTIV, estimated total intracranial volume, CAT12, Computational Anatomy Toolbox for Statistical Parametric Mapping 12, GM, grey matter, medicine.disease, Deep grey matter, NWMV, Normalized white matter volume, MS, Multiple Sclerosis, GMV, grey matter volume, IPS, information processing speed, Neurology (clinical), business, 030217 neurology & neurosurgery, Other subheadings::Other subheadings::/complications [Other subheadings]
Abstract

Highlights • Thalamus atrophy is associated with cognitive impairment in multiple sclerosis. • This was confirmed by automated and manual segmentations, but effect sizes varied. • The algorithms work in a multi-center setting. • Automated techniques exhibit proportional bias with respect to thalamus size. • Differences between vendors can affect the robustness of these associations., Background and rationale Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Methods Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. Results In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values

Published
2021

18. Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis : an evaluation of four automated methods against manual reference segmentations in a multi-center cohort

Author

de Sitter, Alexandra, Verhoeven, Tom, Burggraaff, Jessica, Liu, Yaou, Simoes, Jorge, Ruggieri, Serena, Palotai, Miklos, Brouwer, Iman, Versteeg, Adriaan, Wottschel, Viktor, Ropele, Stefan, Rocca, Mara A., Gasperini, Claudio, Gallo, Antonio, Yiannakas, Marios C., Rovira, Alex, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Kappos, Ludwig, Frederiksen, Jette L., Uitdehaag, Bernard M. J., Barkhof, Frederik, Guttmann, Charles R. G., Vrenken, Hugo, Universitat Autònoma de Barcelona, de Sitter, Alexandra, Verhoeven, Tom, Burggraaff, Jessica, Liu, Yaou, Simoes, Jorge, Ruggieri, Serena, Palotai, Miklo, Brouwer, Iman, Versteeg, Adriaan, Wottschel, Viktor, Ropele, Stefan, Rocca, Mara A, Gasperini, Claudio, Gallo, Antonio, Yiannakas, Marios C, Rovira, Alex, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Kappos, Ludwig, Frederiksen, Jette L, Uitdehaag, Bernard M J, Barkhof, Frederik, Guttmann, Charles R G, Vrenken, Hugo, de Sitter, A., Verhoeven, T., Burggraaff, J., Liu, Y., Simoes, J., Ruggieri, S., Palotai, M., Brouwer, I., Versteeg, A., Wottschel, V., Ropele, S., Rocca, M. A., Gasperini, C., Gallo, A., Yiannakas, M. C., Rovira, A., Enzinger, C., Filippi, M., De Stefano, N., Kappos, L., Frederiksen, J. L., Uitdehaag, B. M. J., Barkhof, F., Guttmann, C. R. G., Vrenken, H., Radiology and nuclear medicine, Neurology, and Amsterdam Neuroscience - Brain Imaging

Subjects
Correlation coefficient, Caudate nucleus, Grey matter, 030218 nuclear medicine & medical imaging, Multiple sclerosis, 03 medical and health sciences, 0302 clinical medicine, Atrophy, Automated segmentation methods, Deep grey matter, Medicine, Humans, Segmentation, Multiple sclerosi, Gray Matter, Neuroradiology, Original Communication, business.industry, Putamen, Brain, medicine.disease, Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, Brain size, Automated segmentation method, Neurology (clinical), business, Nuclear medicine, 030217 neurology & neurosurgery, Human
Abstract

Background Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied. Methods On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed. Results ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations. Conclusions Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

Published
2020

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