Results 51 to 60 of about 184,556 (342)

Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI.

Diagnostic and Interventional Imaging, 2020
PURPOSE The purpose of this study was to create an algorithm that combines multiple machine-learning techniques to predict the expanded disability status scale (EDSS) score of patients with multiple sclerosis at two years solely based on age, sex and ...
P. Roca, A. Attyé, L. Colas, A. Tucholka, P. Rubini, S. Cackowski, J. Ding, J. Budzik, F. Renard, S. Doyle, Emmanuel L Barbier, I. Bousaid, R. Casey, S. Vukusic, N. Lassau, S. Verclytte, F. Cotton, B. Brochet, J. de Seze, P. Douek, F. Guillemin, D. Laplaud, C. Lebrun-Frénay, L. Mansuy, T. Moreau, J. Olaiz, J. Pelletier, C. Rigaud-Bully, B. Stankoff, R. Marignier, M. Debouverie, G. Edan, J. Ciron, A. Ruet, N. Collongues, C. Lubetzki, P. Vermersch, P. Labauge, G. Defer, M. Cohen, A. Fromont, S. Wiertlewsky, E. Berger, P. Clavelou, B. Audoin, C. Giannesini, O. Gout, E. Thouvenot, O. Heinzlef, A. Al-Khedr, B. Bourre, O. Casez, P. Cabre, A. Montcuquet, A. Créange, J. Camdessanche, J. Faure, A. Maurousset, I. Patry, K. Hankiewicz, C. Pottier, N. Maubeuge, C. Labeyrie, C. Nifle, R. Ameli, R. Anxionnat, E. Bannier, C. Barillot, D. Ben Salem, M. Boncoeur-Martel, F. Bonneville, C. Boutet, J. Brisset, F. Cervenanski, B. Claise, O. Commowick, J. Constans, P. Dardel, H. Desal, V. Dousset, F. Durand-Dubief, J. Ferré, E. Gerardin, T. Glattard, S. Grand, T. Grenier, R. Guillevin, C. Guttmann, A. Krainik, S. Kremer, S. Lion, N. Menjot de Champfleur, L. Mondot, O. Outteryck, N. Pyatigorskaya, J. Pruvo, S. Rabasté, J. Ranjeva, J. Roch, J. Sadik, D. Sappey-Marinier, J. Savatovsky, J. Tanguy, A. Tourbah, T. Tourdias   +104 more
semanticscholar   +1 more source

FLAIR and ADC Image-Based Radiomics Features as Predictive Biomarkers of Unfavorable Outcome in Patients With Acute Ischemic Stroke

Frontiers in Neuroscience, 2021
At present, it is still challenging to predict the clinical outcome of acute ischemic stroke (AIS). In this retrospective study, we explored whether radiomics features extracted from fluid-attenuated inversion recovery (FLAIR) and apparent diffusion ...
Guanmin Quan, Ranran Ban, Jialiang Ren, Yawu Liu, Weiwei Wang, Shi-peng Dai, T. Yuan   +6 more
semanticscholar   +1 more source

FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis

NeuroImage: Clinical, 2021
Background: MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor.
O. Goodkin, F. Prados, S.B. Vos, H. Pemberton, S. Collorone, M.H.J. Hagens, M.J. Cardoso, T.A. Yousry, J.S. Thornton, C.H. Sudre, F. Barkhof   +10 more
doaj   +1 more source

DeepGRAI (Deep Gray Rating via Artificial Intelligence): Fast, feasible, and clinically relevant thalamic atrophy measurement on clinical quality T2-FLAIR MRI in multiple sclerosis

NeuroImage: Clinical, 2021
Highlights • We describe a deep learning approach for thalamic measurement on clinical T2-FLAIR.• The algorithm performs comparably to FSL FIRST on high resolution T1.• Clinical relevance on disability and cognition is confirmed.
M. Dwyer, Cassondra Lyman, Hannah Ferrari, N. Bergsland, T. Fuchs, D. Jakimovski, F. Schweser, B. Weinstock-Guttmann, R. Benedict, Jon V. Riolo, Diego G. Silva, R. Zivadinov   +11 more
semanticscholar   +1 more source

Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. [PDF]

, 2020
We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings.
Aboian, Mariam, Cha, Soonmee, Payabvash, Seyedmehdi, Tihan, Tarik   +3 more
core   +1 more source

Radiomic signature of DWI‐FLAIR mismatch in large vessel occlusion stroke

Journal of Neuroimaging, 2021
Ischemic diffusion‐weighted imaging‐fluid‐attenuated inversion recovery (DWI‐FLAIR) mismatch may be useful in guiding acute stroke treatment decisions given its relationship to onset time and parenchymal viability; however, it relies on subjective ...
R. Regenhardt, Martin Bretzner, M.C. Zanon Zotin, A. Bonkhoff, M. Etherton, Sun-Beom Hong, Alvin S. Das, Naif M Alotaibi, J. Vranic, A. Dmytriw, C. Stapleton, Aman B. Patel, Grégory Kuchcinski, N. Rost, T. Leslie-Mazwi   +14 more
semanticscholar   +1 more source

The Diagnostic Utility of Contrast-Enhanced FLAIR Imaging in the Diagnosis of Pediatric Uveitis

Journal of the Belgian Society of Radiology
Objectives: Contrast-enhanced FLAIR fat-suppressed (CE-FLAIR-FS) imaging can potentially increase the diagnostic accuracy of uveal diseases and ultimately provide better patient management.
Saffet Ozturk, Esin Kurtulus Ozturk, Yasemin Tasci Yildiz, Bahadir Konuskan   +3 more
doaj   +1 more source

Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR MRI

Brain Science, 2021
White-matter hyperintensity (WMH) is a primary biomarker for small-vessel cerebrovascular disease, Alzheimer’s disease (AD), and others. The association of WMH with brain structural changes has also recently been reported.
ZunHyan Rieu, JeeYoung Kim, Regina E. Y. Kim, M. Lee, M. Lee, S. Oh, Sheng-Min Wang, Nak-Young Kim, D. Kang, H. Lim, Donghyeon Kim   +10 more
semanticscholar   +1 more source

Simple quantitative measurement based on DWI to objectively judge DWI-FLAIR mismatch in a canine stroke model

Diagnostic and Interventional Radiology, 2015
PURPOSEDiffusion-weighted imaging (DWI) - fluid attenuated inversion recovery (FLAIR) mismatch was proven useful to time the onset of wake-up stroke; however, identifying the status of FLAIR imaging has been mostly subjective.
Sheng Liu, Xiaoquan Xu, Qiguang Cheng, Qingquan Zu, Shanshan Lu, Jing Yu, Xinglong Liu, Bin Wang, Gaojun Teng, Haibin Shi   +9 more
doaj   +1 more source

International Veterinary Epilepsy Task Force recommendations for a veterinary epilepsy-specific MRI protocol [PDF]

, 2015
Epilepsy is one of the most common chronic neurological diseases in veterinary practice. Magnetic resonance imaging (MRI) is regarded as an important diagnostic test to reach the diagnosis of idiopathic epilepsy.
A Bathen-Noethen, A Ciantis De, A Pakozdy, AA Raymond, AJ Hall, AK Shah, Akos Pakozdy, AM McIntosh, Andrea Fischer, Andrea Tipold, AR Deibler, CJ Abramson, Clare Rusbridge, DK Thompson, E Wagner, Edward E. Patterson, EJ Ives, EL So, F Cendes, F Rosenow, GS Berns, GS Berns, H Goldberg, Heidrun Potschka, Holger A. Volk, I Craven, J Laothamatas, J Oertzen Von, J Velmurugan, J Wellmer, J Wellmer, Jacques Penderis, Jelena Jovanovik, JS Duncan, JW Wheless, K Ono, Karen Muñana, Kaspar Matiasek, KL Stadler, KM Casey, KM Fiest, L Gygax, L Heske, L Kearsley-Fleet, LM Mellema, LS Garosi, Luisa De Risio, M Guye, M Konar, M Schwartz, Marjorie Milne, ME Milne, Mette Berendt, Michael Podell, MJ Fernandes, MS Anaya Garcia, N Farid, N Ogata, NK Focke, O Jacqmot, P Singh, P Szmuk, PF Cook, PM Smith, PT Wei, R Goncalves, R Kuzniecky, R Neppl, R Vasta, R Viitmaa, R Viitmaa, RA Thames, RL Wolf, Robyn G. Farqhuar, S Hecht, S Mizoguchi, S Torisu, Sam Long, SD Shorvon, Simon Platt, Sofie F. M. Bhatti, SP Knowler, T Guadalupe, T Hammen, T Kuwabara, TJ Oertzen von, TS Jokinen, U Ebert, V Jantti, V Martle, Veronika M. Stein, WD Gaillard, YM Lim, YS Ding, Z Haneef   +94 more
core   +6 more sources