{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T08:58:13Z","timestamp":1781081893934,"version":"3.54.1"},"reference-count":103,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiol Phys Technol"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s12194-026-01046-w","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T09:35:12Z","timestamp":1778492112000},"page":"440-453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic MRI and radiomics in multiple sclerosis: improving precision in diagnosis and prognosis"],"prefix":"10.1007","volume":"19","author":[{"given":"Mohammad Mehdi","family":"Azizi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Korosh","family":"Saber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rasool","family":"Azmoonfar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8538-1898","authenticated-orcid":false,"given":"Daryoush","family":"Shahbazi-Gahrouei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"1046_CR1","doi-asserted-by":"publisher","first-page":"175628642110076","DOI":"10.1177\/17562864211007687","volume":"14","author":"S Haase","year":"2021","unstructured":"Haase S, Linker RA. Inflammation in multiple sclerosis. Ther Adv Neurol Disord. 2021;14:17562864211007687.","journal-title":"Ther Adv Neurol Disord"},{"issue":"1","key":"1046_CR2","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1186\/s12883-021-02344-z","volume":"21","author":"S Gustavsen","year":"2021","unstructured":"Gustavsen S, Olsson A, S\u00f8ndergaard HB, Andresen SR, S\u00f8rensen PS, Sellebjerg F, et al. The association of selected multiple sclerosis symptoms with disability and quality of life: a large Danish self-report survey. BMC Neurol. 2021;21(1):317.","journal-title":"BMC Neurol"},{"issue":"20","key":"1046_CR3","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1001\/jama.2022.14236","volume":"328","author":"R Marcus","year":"2022","unstructured":"Marcus R. What is multiple sclerosis? JAMA. 2022;328(20):2078.","journal-title":"JAMA"},{"key":"1046_CR4","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s00062-015-0430-y","volume":"25","author":"M Wattjes","year":"2015","unstructured":"Wattjes M, Steenwijk M, Stangel M. MRI in the diagnosis and monitoring of multiple sclerosis: an update. Clin Neuroradiol. 2015;25:157\u201365.","journal-title":"Clin Neuroradiol"},{"key":"1046_CR5","unstructured":"Shin H-G, Dewey BE, Brabec J, Zhang J, Ezzedin O, Ecoff K, et al. editors. Identifying Multiple Sclerosis Lesion Subtypes with Distinct Microstructural Features using Advanced Microstructural MRI. MULTIPLE SCLEROSIS JOURNAL; 2023: SAGE PUBLICATIONS LTD 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND."},{"issue":"1","key":"1046_CR6","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1177\/08465371241261847","volume":"76","author":"\u00c0 Rovira","year":"2025","unstructured":"Rovira \u00c0, Auger C, Sceppacuercia S, Torres C. Typical and emerging diagnostic MRI features in multiple sclerosis. Can Assoc Radiol J. 2025;76(1):122\u201344.","journal-title":"Can Assoc Radiol J"},{"issue":"10","key":"1046_CR7","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1016\/S1474-4422(25)00270-4","volume":"24","author":"X Montalban","year":"2025","unstructured":"Montalban X, Lebrun-Fr\u00e9nay C, Oh J, Arrambide G, Moccia M, Amato MP, et al. Diagnosis of multiple sclerosis: 2024 revisions of the McDonald criteria. Lancet Neurol. 2025;24(10):850\u201365.","journal-title":"Lancet Neurol"},{"key":"1046_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/imag_a_00357","volume":"2","author":"L Sundvall","year":"2024","unstructured":"Sundvall L, Mikkelsen IK, Eskildsen SF, Hj\u00f8rringgaard MM, Nygaard M, Rasmussen PV, et al. Differentiation of MS lesions through analysis of microvascular distribution. Imaging Neurosci. 2024;2:1\u201316.","journal-title":"Imaging Neurosci"},{"issue":"6","key":"1046_CR9","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.3174\/ajnr.A5227","volume":"38","author":"LN Tanenbaum","year":"2017","unstructured":"Tanenbaum LN, Tsiouris AJ, Johnson AN, Naidich TP, DeLano MC, Melhem ER, et al. Synthetic MRI for clinical neuroimaging: results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial. Am J Neuroradiol. 2017;38(6):1103\u201310.","journal-title":"Am J Neuroradiol"},{"issue":"4","key":"1046_CR10","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1002\/jmri.27440","volume":"55","author":"S Ji","year":"2022","unstructured":"Ji S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: technologies and applications in neuroradiology. J Magn Reson Imaging. 2022;55(4):1013\u201325.","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"1046_CR11","doi-asserted-by":"publisher","first-page":"120","DOI":"10.4103\/abr.abr_581_24","volume":"14","author":"MM Azizi","year":"2025","unstructured":"Azizi MM, Abedi I. The role of synthetic MRI and radiomics in breast cancer: a review. Adv Biomedical Res. 2025;14(1):120.","journal-title":"Adv Biomedical Res"},{"issue":"6","key":"1046_CR12","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.3174\/ajnr.A4665","volume":"37","author":"T Granberg","year":"2016","unstructured":"Granberg T, Uppman M, Hashim F, Cananau C, Nordin L, Shams S, et al. Clinical feasibility of synthetic MRI in multiple sclerosis: a diagnostic and volumetric validation study. Am J Neuroradiol. 2016;37(6):1023\u20139.","journal-title":"Am J Neuroradiol"},{"issue":"2","key":"1046_CR13","doi-asserted-by":"publisher","first-page":"257","DOI":"10.3174\/ajnr.A5012","volume":"38","author":"A Hagiwara","year":"2017","unstructured":"Hagiwara A, Hori M, Yokoyama K, Takemura MY, Andica C, Tabata T, et al. Synthetic MRI in the detection of multiple sclerosis plaques. Am J Neuroradiol. 2017;38(2):257\u201363.","journal-title":"Am J Neuroradiol"},{"key":"1046_CR14","doi-asserted-by":"crossref","unstructured":"Girdhar S, Nair SS, Thomas B, Kesavadas C. Non-contrast magnetic resonance evaluation of active multiple sclerosis lesions: Emerging role of quantitative synthetic magnetic resonance imaging. Neuroradiol J. 2024:19714009241269541.","DOI":"10.1177\/19714009241269541"},{"key":"1046_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-018-0068-z","volume":"2","author":"S Rizzo","year":"2018","unstructured":"Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol experimental. 2018;2:1\u20138.","journal-title":"Eur Radiol experimental"},{"issue":"2","key":"1046_CR16","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563\u201377.","journal-title":"Radiology"},{"issue":"4","key":"1046_CR17","doi-asserted-by":"publisher","first-page":"488","DOI":"10.2967\/jnumed.118.222893","volume":"61","author":"ME Mayerhoefer","year":"2020","unstructured":"Mayerhoefer ME, Materka A, Langs G, H\u00e4ggstr\u00f6m I, Szczypi\u0144ski P, Gibbs P, et al. Introduction to radiomics. J Nucl Med. 2020;61(4):488\u201395.","journal-title":"J Nucl Med"},{"key":"1046_CR18","doi-asserted-by":"publisher","first-page":"106609","DOI":"10.1016\/j.cmpb.2021.106609","volume":"215","author":"AA Ardakani","year":"2022","unstructured":"Ardakani AA, Bureau NJ, Ciaccio EJ, Acharya UR. Interpretation of radiomics features\u2013a pictorial review. Comput Methods Programs Biomed. 2022;215:106609.","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"1046_CR19","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1186\/s12880-024-01528-6","volume":"24","author":"A Rostami","year":"2024","unstructured":"Rostami A, Robatjazi M, Dareyni A, Ghorbani AR, Ganji O, Siyami M, et al. Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques. BMC Med Imaging. 2024;24(1):345.","journal-title":"BMC Med Imaging"},{"key":"1046_CR20","doi-asserted-by":"crossref","unstructured":"Khan H, Woodruff HC, Giraldo DL, Werthen-Brabants L, Mali SA, Amirrajab S et al. Leveraging Hand-Crafted Radiomics on Multicenter FLAIR MRI for Predicting Disability Progression in People with Multiple Sclerosis. medRxiv. 2025:2025.01. 23.25320971.","DOI":"10.1101\/2025.01.23.25320971"},{"issue":"11","key":"1046_CR21","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.3174\/ajnr.A7274","volume":"42","author":"G Pontillo","year":"2021","unstructured":"Pontillo G, Tommasin S, Cuocolo R, Petracca M, Petsas N, Ugga L, et al. A combined radiomics and machine learning approach to overcome the clinicoradiologic paradox in multiple sclerosis. Am J Neuroradiol. 2021;42(11):1927\u201333.","journal-title":"Am J Neuroradiol"},{"issue":"8","key":"1046_CR22","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1097\/RLI.0000000000000962","volume":"58","author":"A Hagiwara","year":"2023","unstructured":"Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: from simultaneous rapid acquisition methods and analysis techniques using scoring, machine learning, radiomics, and deep learning to the generation of novel metrics. Invest Radiol. 2023;58(8):548\u201360.","journal-title":"Invest Radiol"},{"key":"1046_CR23","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jocn.2021.02.018","volume":"87","author":"A Hagiwara","year":"2021","unstructured":"Hagiwara A, Otsuka Y, Andica C, Kato S, Yokoyama K, Hori M, et al. Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorders by multiparametric quantitative MRI using convolutional neural network. J Clin Neurosci. 2021;87:55\u20138.","journal-title":"J Clin Neurosci"},{"key":"1046_CR24","doi-asserted-by":"crossref","unstructured":"Abujamea AH, Albadr FB, Asiri AM. The Value of Using Quantitative MRI based on Synthetic Acquisition and Apparent Diffusion Coefficient to Monitor Multiple Sclerosis Lesion Activity. Curr Med Imaging. 2025:e15734056343086.","DOI":"10.2174\/0115734056343086250103020830"},{"issue":"10","key":"1046_CR25","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1097\/RLI.0000000000000365","volume":"52","author":"A Hagiwara","year":"2017","unstructured":"Hagiwara A, Warntjes M, Hori M, Andica C, Nakazawa M, Kumamaru KK, et al. SyMRI of the Brain: Rapid Quantification of Relaxation Rates and Proton Density, With Synthetic MRI, Automatic Brain Segmentation, and Myelin Measurement. Invest Radiol. 2017;52(10):647\u201357.","journal-title":"Invest Radiol"},{"key":"1046_CR26","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.mri.2022.03.005","volume":"90","author":"R Kose","year":"2022","unstructured":"Kose R, Kose K, Terada Y. Implementation of the QRAPMASTER data analysis using dictionary matching and quantitative evaluation of the magnetization transfer effect. Magn Reson Imaging. 2022;90:26\u201336.","journal-title":"Magn Reson Imaging"},{"issue":"15","key":"1046_CR27","doi-asserted-by":"publisher","first-page":"3624","DOI":"10.3390\/cancers14153624","volume":"14","author":"AS Konar","year":"2022","unstructured":"Konar AS, Paudyal R, Shah AD, Fung M, Banerjee S, Dave A, et al. Qualitative and Quantitative Performance of Magnetic Resonance Image Compilation (MAGiC) Method: An Exploratory Analysis for Head and Neck Imaging. Cancers. 2022;14(15):3624.","journal-title":"Cancers"},{"issue":"1","key":"1046_CR28","doi-asserted-by":"publisher","first-page":"56","DOI":"10.2463\/mrms.mp.2018-0144","volume":"19","author":"L Chougar","year":"2020","unstructured":"Chougar L, Hagiwara A, Takano N, Andica C, Cohen-Adad J, Warntjes M, et al. Signal Intensity within Cerebral Venous Sinuses on Synthetic MRI. Magn Reson Med Sci. 2020;19(1):56\u201363.","journal-title":"Magn Reson Med Sci"},{"issue":"5","key":"1046_CR29","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1002\/mrm.29939","volume":"91","author":"S Fujita","year":"2024","unstructured":"Fujita S, Gagoski B, Hwang KP, Hagiwara A, Warntjes M, Fukunaga I, et al. Cross-vendor multiparametric mapping of the human brain using 3D\u2010QALAS: A multicenter and multivendor study. Magn Reson Med. 2024;91(5):1863\u201375.","journal-title":"Magn Reson Med"},{"issue":"7","key":"1046_CR30","doi-asserted-by":"publisher","first-page":"4861","DOI":"10.1002\/mp.15686","volume":"49","author":"KP Hwang","year":"2022","unstructured":"Hwang KP, Fujita S, Synthetic MR. physical principles, clinical implementation, and new developments. Med Phys. 2022;49(7):4861\u201374.","journal-title":"Med Phys"},{"key":"1046_CR31","doi-asserted-by":"publisher","first-page":"117561","DOI":"10.1016\/j.neuroimage.2020.117561","volume":"226","author":"CW van der Weijden","year":"2021","unstructured":"van der Weijden CW, Garc\u00eda DV, Borra RJ, Thurner P, Meilof JF, van Laar P-J, et al. Myelin quantification with MRI: a systematic review of accuracy and reproducibility. NeuroImage. 2021;226:117561.","journal-title":"NeuroImage"},{"key":"1046_CR32","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.mri.2023.11.015","volume":"106","author":"S Kumar","year":"2024","unstructured":"Kumar S, Saber H, Charron O, Freeman L, Tamir JI. Correcting synthetic MRI contrast-weighted images using deep learning. Magn Reson Imaging. 2024;106:43\u201354.","journal-title":"Magn Reson Imaging"},{"issue":"5","key":"1046_CR33","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.3390\/tomography9050137","volume":"9","author":"L Peretti","year":"2023","unstructured":"Peretti L, Donatelli G, Cencini M, Cecchi P, Buonincontri G, Cosottini M, et al. Generating synthetic radiological images with PySynthMRI: an open-source cross-platform tool. Tomography. 2023;9(5):1723\u201333.","journal-title":"Tomography"},{"issue":"2","key":"1046_CR34","doi-asserted-by":"publisher","first-page":"144","DOI":"10.3934\/Neuroscience.2023011","volume":"10","author":"NS Nasser","year":"2023","unstructured":"Nasser NS, Sharma K, Mehta PM, Mahajan V, Mahajan H, Venugopal VK. Estimation of white matter hyperintensities with synthetic MRI myelin volume fraction in patients with multiple sclerosis and non-multiple-sclerosis white matter hyperintensities: a pilot study among the Indian population. AIMS Neurosci. 2023;10(2):144.","journal-title":"AIMS Neurosci"},{"key":"1046_CR35","first-page":"01","volume":"82","author":"N Braga","year":"2024","unstructured":"Braga N, Aymerich X, Alonso J, Mongay-Ochoa N, Pareto D, Montalban X, et al. Synthetic MRI in progressive MS: associations with disability. Arq Neuropsiquiatr. 2024;82:01.","journal-title":"Arq Neuropsiquiatr"},{"issue":"3","key":"1046_CR36","doi-asserted-by":"publisher","first-page":"471","DOI":"10.3174\/ajnr.A6930","volume":"42","author":"S Fujita","year":"2021","unstructured":"Fujita S, Yokoyama K, Hagiwara A, Kato S, Andica C, Kamagata K, et al. 3D quantitative synthetic MRI in the evaluation of multiple sclerosis lesions. Am J Neuroradiol. 2021;42(3):471\u20138.","journal-title":"Am J Neuroradiol"},{"issue":"2","key":"1046_CR37","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1515\/dx-2024-0168","volume":"12","author":"T Pires","year":"2025","unstructured":"Pires T, Pendem S, Priyanka MMJ. Technical aspects and clinical applications of synthetic MRI: a scoping review. Diagnosis. 2025;12(2):163\u201374.","journal-title":"Diagnosis"},{"issue":"10","key":"1046_CR38","first-page":"1642","volume":"40","author":"A Hagiwara","year":"2019","unstructured":"Hagiwara A, Kamagata K, Shimoji K, Yokoyama K, Andica C, Hori M, et al. White matter abnormalities in multiple sclerosis evaluated by quantitative synthetic MRI, diffusion tensor imaging, and neurite orientation dispersion and density imaging. Am J Neuroradiol. 2019;40(10):1642\u20138.","journal-title":"Am J Neuroradiol"},{"issue":"2","key":"1046_CR39","doi-asserted-by":"publisher","first-page":"393","DOI":"10.3390\/cells9020393","volume":"9","author":"L Saccenti","year":"2020","unstructured":"Saccenti L, Hagiwara A, Andica C, Yokoyama K, Fujita S, Kato S, et al. Myelin measurement using quantitative magnetic resonance imaging: a correlation study comparing various imaging techniques in patients with multiple sclerosis. Cells. 2020;9(2):393.","journal-title":"Cells"},{"issue":"11","key":"1046_CR40","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1007\/s00234-019-02241-w","volume":"61","author":"L Saccenti","year":"2019","unstructured":"Saccenti L, Andica C, Hagiwara A, Yokoyama K, Takemura MY, Fujita S, et al. Brain tissue and myelin volumetric analysis in multiple sclerosis at 3T MRI with various in-plane resolutions using synthetic MRI. Neuroradiology. 2019;61(11):1219\u201327.","journal-title":"Neuroradiology"},{"key":"1046_CR41","doi-asserted-by":"crossref","unstructured":"Schneider R, Matusche B, Ladopoulos T, Ayzenberg I, Biesalski AS, Gold R et al. Quantification of individual remyelination during short-term disease course by synthetic magnetic resonance imaging. Brain Commun. 2022;4(4).","DOI":"10.1093\/braincomms\/fcac172"},{"key":"1046_CR42","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s10334-019-00804-9","volume":"33","author":"F Di Giuliano","year":"2020","unstructured":"Di Giuliano F, Minosse S, Picchi E, Marfia GA, Da Ros V, Muto M, et al. Comparison between synthetic and conventional magnetic resonance imaging in patients with multiple sclerosis and controls. Magn Reson Mater Phys Biol Med. 2020;33:549\u201357.","journal-title":"Magn Reson Mater Phys Biol Med"},{"issue":"12","key":"1046_CR43","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1007\/s00234-022-02973-2","volume":"64","author":"FX Aymerich","year":"2022","unstructured":"Aymerich FX, Auger C, Alonso J, Barros A, Clarke MA, Mora J, et al. Assessment of 2D conventional and synthetic MRI in multiple sclerosis. Neuroradiology. 2022;64(12):2315\u201322.","journal-title":"Neuroradiology"},{"key":"1046_CR44","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","volume":"167","author":"JP Fortin","year":"2018","unstructured":"Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage. 2018;167:104\u201320.","journal-title":"NeuroImage"},{"key":"1046_CR45","doi-asserted-by":"crossref","unstructured":"Essel RR, Krieger B, Bellenberg B, M\u00fcller D, Ladopoulos T, Gold R et al. Lesion assessment in multiple sclerosis: a comparison between synthetic and conventional fluid-attenuated inversion recovery imaging. Front Neurol. 2025;16.","DOI":"10.3389\/fneur.2025.1537465"},{"issue":"1","key":"1046_CR46","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","volume":"11","author":"JE van Timmeren","year":"2020","unstructured":"van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging\u2014how-to guide and critical reflection. Insights into Imaging. 2020;11(1):91.","journal-title":"Insights into Imaging"},{"key":"1046_CR47","first-page":"429","volume-title":"Handbook of Medical Image Computing and Computer","author":"MPA Starmans","year":"2020","unstructured":"Starmans MPA, van der Voort SR, Castillo Tovar JM, Veenland JF, Klein S, Niessen WJ. Chapter 18 - Radiomics: Data mining using quantitative medical image features. In: Zhou SK, Rueckert D, Fichtinger G, editors. Handbook of Medical Image Computing and Computer. Assisted Intervention: Academic; 2020. pp. 429\u201356."},{"key":"1046_CR48","doi-asserted-by":"crossref","unstructured":"Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A et al. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020;93(1108).","DOI":"10.1259\/bjr.20190948"},{"issue":"2","key":"1046_CR49","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, Aerts HJ, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328\u201338.","journal-title":"Radiology"},{"key":"1046_CR50","doi-asserted-by":"publisher","first-page":"102989","DOI":"10.1016\/j.msard.2021.102989","volume":"53","author":"Y Peng","year":"2021","unstructured":"Peng Y, Zheng Y, Tan Z, Liu J, Xiang Y, Liu H, et al. Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach. Multiple Scler Relat disorders. 2021;53:102989.","journal-title":"Multiple Scler Relat disorders"},{"key":"1046_CR51","doi-asserted-by":"crossref","unstructured":"Yan Z, Yuan S, Zhu Q, Wang X, Shi Z, Zhang Y et al. Radiomics models based on cortical damages for identification of multiple sclerosis with cognitive impairment. Multiple Scler Relat Disorders. 2024;81.","DOI":"10.1016\/j.msard.2023.105348"},{"key":"1046_CR52","doi-asserted-by":"crossref","unstructured":"Orzan F, Iancu \u015eD, Dio\u015fan L, B\u00e1lint Z. Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis \u2013 a systematic review. Front NeuroSci. 2025;18.","DOI":"10.3389\/fnins.2024.1457420"},{"key":"1046_CR53","doi-asserted-by":"publisher","first-page":"105363","DOI":"10.1016\/j.msard.2023.105363","volume":"82","author":"F Shekari","year":"2024","unstructured":"Shekari F, Vard A, Adibi I, Danesh-Mobarhan S. Investigating the feasibility of differentiating MS active lesions from inactive ones using texture analysis and machine learning methods in DWI images. Multiple Scler Relat Disorders. 2024;82:105363.","journal-title":"Multiple Scler Relat Disorders"},{"issue":"5","key":"1046_CR54","doi-asserted-by":"publisher","first-page":"055004","DOI":"10.1088\/2057-1976\/ace261","volume":"9","author":"B Khajetash","year":"2023","unstructured":"Khajetash B, Talebi A, Bagherpour Z, Abbaspour S, Tavakoli M. Introducing radiomics model to predict active plaque in multiple sclerosis patients using magnetic resonance images. Biomedical Phys Eng Express. 2023;9(5):055004.","journal-title":"Biomedical Phys Eng Express"},{"issue":"1","key":"1046_CR55","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/jmrs.662","volume":"70","author":"M Elmahdy","year":"2023","unstructured":"Elmahdy M, Sebro R. Radiomics analysis in medical imaging research. J Med Radiat Sci. 2023;70(1):3\u20137.","journal-title":"J Med Radiat Sci"},{"issue":"1","key":"1046_CR56","doi-asserted-by":"publisher","first-page":"100068","DOI":"10.1016\/j.metrad.2024.100068","volume":"2","author":"J Feng","year":"2024","unstructured":"Feng J, Huang Y, Zhang X, Yang Q, Guo Y, Xia Y, et al. Research and application progress of radiomics in neurodegenerative diseases. Meta-Radiology. 2024;2(1):100068.","journal-title":"Meta-Radiology"},{"issue":"12","key":"1046_CR57","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RT, Deist TM, Peerlings J, De Jong EE, Van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat reviews Clin Oncol. 2017;14(12):749\u201362.","journal-title":"Nat reviews Clin Oncol"},{"key":"1046_CR58","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.jocn.2021.05.015","volume":"90","author":"B Taha","year":"2021","unstructured":"Taha B, Boley D, Sun J, Chen C. Potential and limitations of radiomics in neuro-oncology. J Clin Neurosci. 2021;90:206\u201311.","journal-title":"J Clin Neurosci"},{"issue":"9","key":"1046_CR59","doi-asserted-by":"publisher","first-page":"4670","DOI":"10.1007\/s00330-019-06026-w","volume":"29","author":"Y Liu","year":"2019","unstructured":"Liu Y, Dong D, Zhang L, Zang Y, Duan Y, Qiu X, et al. Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder. Eur Radiol. 2019;29(9):4670\u20137.","journal-title":"Eur Radiol"},{"issue":"1","key":"1046_CR60","doi-asserted-by":"publisher","first-page":"014504","DOI":"10.1117\/1.JMI.8.1.014504","volume":"8","author":"A Fenneteau","year":"2021","unstructured":"Fenneteau A, Bourdon P, Helbert D, Fernandez-Maloigne C, Habas C, Guillevin R. Investigating efficient CNN architecture for multiple sclerosis lesion segmentation. J Med Imaging. 2021;8(1):014504.","journal-title":"J Med Imaging"},{"issue":"5","key":"1046_CR61","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1049\/ipr2.12419","volume":"16","author":"R Wang","year":"2022","unstructured":"Wang R, Lei T, Cui R, Zhang B, Meng H, Nandi AK. Medical image segmentation using deep learning: A survey. IET Image Proc. 2022;16(5):1243\u201367.","journal-title":"IET Image Proc"},{"issue":"20","key":"1046_CR62","doi-asserted-by":"publisher","first-page":"2470","DOI":"10.3390\/electronics10202470","volume":"10","author":"D Bhatt","year":"2021","unstructured":"Bhatt D, Patel C, Talsania H, Patel J, Vaghela R, Pandya S, et al. CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics. 2021;10(20):2470.","journal-title":"Electronics"},{"issue":"4","key":"1046_CR63","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.irbm.2021.06.003","volume":"43","author":"MO Khairandish","year":"2022","unstructured":"Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ. A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images. IRBM. 2022;43(4):290\u20139.","journal-title":"IRBM"},{"key":"1046_CR64","doi-asserted-by":"publisher","first-page":"104750","DOI":"10.1016\/j.msard.2023.104750","volume":"75","author":"S Du","year":"2023","unstructured":"Du S, Yuan C, Zhou Q, Huang X, Meng H, Chen M, et al. Deep learning-based PET\/MR radiomics for the classification of annualized relapse rate in multiple sclerosis. Multiple Scler Relat Disorders. 2023;75:104750.","journal-title":"Multiple Scler Relat Disorders"},{"issue":"1","key":"1046_CR65","doi-asserted-by":"publisher","first-page":"67","DOI":"10.61186\/shefa.12.1.67","volume":"12","author":"F Samifar","year":"2023","unstructured":"Samifar F, Samifar S, Vafaee F, Gorji A. The Use of Artificial Intelligence in the Evaluation of Multiple Sclerosis Brain Lesions Through the Processing of MRI Images. Neurosci J Shefaye Khatam. 2023;12(1):67\u201384.","journal-title":"Neurosci J Shefaye Khatam"},{"key":"1046_CR66","unstructured":"Soleymani M. Deep learning methods for classifying disease subtypes in multiple sclerosis based on clinical imaging and non-imaging data. 2024."},{"key":"1046_CR67","doi-asserted-by":"crossref","unstructured":"Lavrova E, Lommers E, Woodruff HC, Chatterjee A, Maquet P, Salmon E et al. Exploratory Radiomic Analysis of Conventional vs. Quantitative Brain MRI: Toward Automatic Diagnosis of Early Multiple Sclerosis. Front NeuroSci. 2021;15.","DOI":"10.3389\/fnins.2021.679941"},{"issue":"5","key":"1046_CR68","doi-asserted-by":"publisher","first-page":"e70085","DOI":"10.1111\/jon.70085","volume":"35","author":"E Dereskewicz","year":"2025","unstructured":"Dereskewicz E, La Rosa F, Dos Santos Silva J, Sizer E, Kohli A, Wynen M, et al. A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation. J Neuroimaging. 2025;35(5):e70085.","journal-title":"J Neuroimaging"},{"key":"1046_CR69","doi-asserted-by":"publisher","first-page":"110078","DOI":"10.1016\/j.compbiomed.2025.110078","volume":"192","author":"S Umirzakova","year":"2025","unstructured":"Umirzakova S, Shakhnoza M, Sevara M, Whangbo TK. Deep learning for multiple sclerosis lesion classification and stratification using MRI. Comput Biol Med. 2025;192:110078.","journal-title":"Comput Biol Med"},{"issue":"10","key":"1046_CR70","doi-asserted-by":"publisher","first-page":"6543","DOI":"10.1007\/s00415-024-12651-3","volume":"271","author":"H Yousef","year":"2024","unstructured":"Yousef H, Malagurski Tortei B, Castiglione F. Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review. J Neurol. 2024;271(10):6543\u201372.","journal-title":"J Neurol"},{"issue":"1","key":"1046_CR71","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.acra.2024.08.026","volume":"32","author":"X Wang","year":"2025","unstructured":"Wang X, Liu S, Yan Z, Yin F, Feng J, Liu H, et al. Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis. Acad Radiol. 2025;32(1):411\u201324.","journal-title":"Acad Radiol"},{"key":"1046_CR72","doi-asserted-by":"publisher","first-page":"679941","DOI":"10.3389\/fnins.2021.679941","volume":"15","author":"E Lavrova","year":"2021","unstructured":"Lavrova E, Lommers E, Woodruff HC, Chatterjee A, Maquet P, Salmon E, et al. Exploratory Radiomic Analysis of Conventional vs. Quantitative Brain MRI: Toward Automatic Diagnosis of Early Multiple Sclerosis. Front Neurosci. 2021;15:679941.","journal-title":"Front Neurosci"},{"key":"1046_CR73","doi-asserted-by":"crossref","unstructured":"Yan Z, Liu H, Chen X, Zheng Q, Zeng C, Zheng Y et al. Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder. Front NeuroSci. 2021;15.","DOI":"10.3389\/fnins.2021.765634"},{"issue":"6","key":"1046_CR74","doi-asserted-by":"publisher","first-page":"fcae395","DOI":"10.1093\/braincomms\/fcae395","volume":"6","author":"BM Keegan","year":"2024","unstructured":"Keegan BM, Absinta M, Cohen-Adad J, Flanagan EP, Henry RG, Klawiter EC, et al. Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future. Brain Commun. 2024;6(6):fcae395.","journal-title":"Brain Commun"},{"issue":"1\u20132","key":"1046_CR75","first-page":"30","volume":"39","author":"T Akbarpour","year":"2017","unstructured":"Akbarpour T, Shamsi M, Daneshvar S, Pooreisa M. Unsupervised multimodal magnetic resonance images segmentation and multiple sclerosis lesions extraction based on edge and texture features. Appl Med Inf. 2017;39(1\u20132):30\u201340.","journal-title":"Appl Med Inf"},{"issue":"6","key":"1046_CR76","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.1002\/ima.22908","volume":"33","author":"A Pandian","year":"2023","unstructured":"Pandian A, Udhayakumar G. Improved multiple sclerosis diagnosis with advanced deep learning techniques. Int J Imaging Syst Technol. 2023;33(6):2128\u201341.","journal-title":"Int J Imaging Syst Technol"},{"key":"1046_CR77","doi-asserted-by":"publisher","first-page":"102653","DOI":"10.1016\/j.nicl.2021.102653","volume":"30","author":"E Roura","year":"2021","unstructured":"Roura E, Maclair G, Andorr\u00e0 M, Juanals F, Pulido-Valdeolivas I, Saiz A, et al. Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients. NeuroImage: Clin. 2021;30:102653.","journal-title":"NeuroImage: Clin"},{"key":"1046_CR78","doi-asserted-by":"publisher","first-page":"4278","DOI":"10.1109\/TNSRE.2023.3321874","volume":"31","author":"BG Schultz","year":"2023","unstructured":"Schultz BG, Joukhadar Z, Nattala U, del Mar Quiroga M, Noffs G, Rojas S, et al. Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics. IEEE Trans Neural Syst Rehabil Eng. 2023;31:4278\u201385.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"12","key":"1046_CR79","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.3174\/ajnr.A7536","volume":"43","author":"M Tranfa","year":"2022","unstructured":"Tranfa M, Pontillo G, Petracca M, Brunetti A, Tedeschi E, Palma G, et al. Quantitative MRI in multiple sclerosis: from theory to application. Am J Neuroradiol. 2022;43(12):1688\u201395.","journal-title":"Am J Neuroradiol"},{"issue":"2","key":"1046_CR80","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s13246-022-01128-0","volume":"45","author":"Z Zheng","year":"2022","unstructured":"Zheng Z, Yang J, Zhang D, Ma J, Yin H, Liu Y, et al. The effect of scan parameters on T1, T2 relaxation times measured with multi-dynamic multi-echo sequence: a phantom study. Phys Eng Sci Med. 2022;45(2):657\u201364.","journal-title":"Phys Eng Sci Med"},{"key":"1046_CR81","doi-asserted-by":"crossref","unstructured":"Nepi V, Pasini G, Bini F, Marinozzi F, Russo G, Stefano A, editors. MRI-Based Radiomics analysis for identification of features correlated with the expanded disability status scale of multiple sclerosis patients. International Conference on Image Analysis and Processing; 2022: Springer.","DOI":"10.1007\/978-3-031-13321-3_32"},{"key":"1046_CR82","doi-asserted-by":"publisher","first-page":"103166","DOI":"10.1016\/j.nicl.2022.103166","volume":"36","author":"T Ladopoulos","year":"2022","unstructured":"Ladopoulos T, Matusche B, Bellenberg B, Heuser F, Gold R, Lukas C, et al. Relaxometry and brain myelin quantification with synthetic MRI in MS subtypes and their associations with spinal cord atrophy. NeuroImage: Clin. 2022;36:103166.","journal-title":"NeuroImage: Clin"},{"issue":"5","key":"1046_CR83","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1111\/jon.12725","volume":"30","author":"G Mangeat","year":"2020","unstructured":"Mangeat G, Ouellette R, Wabartha M, De Leener B, Platt\u00e9n M, Danylait\u00e9 Karrenbauer V, et al. Machine learning and multiparametric brain MRI to differentiate hereditary diffuse leukodystrophy with spheroids from multiple sclerosis. J Neuroimaging. 2020;30(5):674\u201382.","journal-title":"J Neuroimaging"},{"issue":"1","key":"1046_CR84","doi-asserted-by":"publisher","first-page":"fcae427","DOI":"10.1093\/braincomms\/fcae427","volume":"7","author":"M Gurevich","year":"2025","unstructured":"Gurevich M, Zilkha-Falb R, Sherman J, Usdin M, Raposo C, Craveiro L, et al. Machine learning\u2013based prediction of disease progression in primary progressive multiple sclerosis. Brain Commun. 2025;7(1):fcae427.","journal-title":"Brain Commun"},{"key":"1046_CR85","doi-asserted-by":"publisher","first-page":"1537465","DOI":"10.3389\/fneur.2025.1537465","volume":"16","author":"RR Essel","year":"2025","unstructured":"Essel RR, Krieger B, Bellenberg B, M\u00fcller D, Ladopoulos T, Gold R, et al. Lesion assessment in multiple sclerosis: a comparison between synthetic and conventional fluid-attenuated inversion recovery imaging. Front Neurol. 2025;16:1537465.","journal-title":"Front Neurol"},{"issue":"2","key":"1046_CR86","doi-asserted-by":"publisher","first-page":"224","DOI":"10.3174\/ajnr.A5927","volume":"40","author":"A Hagiwara","year":"2019","unstructured":"Hagiwara A, Otsuka Y, Hori M, Tachibana Y, Yokoyama K, Fujita S, et al. Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation. AJNR Am J Neuroradiol. 2019;40(2):224\u201330.","journal-title":"AJNR Am J Neuroradiol"},{"issue":"5","key":"1046_CR87","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1002\/jmri.26712","volume":"50","author":"K Ryu","year":"2019","unstructured":"Ryu K, Nam Y, Gho SM, Jang J, Lee HJ, Cha J, et al. Data-driven synthetic MRI FLAIR artifact correction via deep neural network. J Magn Reson Imaging. 2019;50(5):1413\u201323.","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"1046_CR88","doi-asserted-by":"publisher","first-page":"11552","DOI":"10.1038\/s41598-022-15204-8","volume":"12","author":"J Andr\u00e9","year":"2022","unstructured":"Andr\u00e9 J, Barrit S, Jissendi P. Synthetic MRI for stroke: a qualitative and quantitative pilot study. Sci Rep. 2022;12(1):11552.","journal-title":"Sci Rep"},{"issue":"24","key":"1046_CR89","doi-asserted-by":"publisher","first-page":"24TR02","DOI":"10.1088\/1361-6560\/aba798","volume":"65","author":"R Da-Ano","year":"2020","unstructured":"Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol. 2020;65(24):24TR02.","journal-title":"Phys Med Biol"},{"key":"1046_CR90","doi-asserted-by":"crossref","unstructured":"Fischer C, Goldberg J, Vulcu S, Wagner F, Sch\u00f6ni D, S\u00f6ll N et al. Nimodipine-Induced Blood Pressure Changes Can Predict Delayed Cerebral Ischemia. Front Neurol. 2019;10.","DOI":"10.3389\/fneur.2019.01161"},{"issue":"20","key":"1046_CR91","doi-asserted-by":"publisher","first-page":"7856","DOI":"10.3390\/s22207856","volume":"22","author":"N Aslam","year":"2022","unstructured":"Aslam N, Khan IU, Bashamakh A, Alghool FA, Aboulnour M, Alsuwayan NM, et al. Multiple sclerosis diagnosis using machine learning and deep learning: challenges and opportunities. Sensors. 2022;22(20):7856.","journal-title":"Sensors"},{"issue":"2","key":"1046_CR92","doi-asserted-by":"publisher","first-page":"172","DOI":"10.2967\/jnumed.121.262464","volume":"63","author":"F Orlhac","year":"2022","unstructured":"Orlhac F, Eertink JJ, Cottereau AS, Zijlstra JM, Thieblemont C, Meignan M, et al. A Guide to ComBat Harmonization of Imaging Biomarkers in Multicenter Studies. J Nucl Med. 2022;63(2):172\u20139.","journal-title":"J Nucl Med"},{"key":"1046_CR93","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1111\/vru.13147","volume":"63","author":"G Currie","year":"2022","unstructured":"Currie G, Rohren E. The deep radiomic analytics pipeline. Vet Radiol Ultrasound. 2022;63:889\u201396.","journal-title":"Vet Radiol Ultrasound"},{"key":"1046_CR94","unstructured":"Lavrova E, Lommers E, Woodruff HC, Chatterjee A, Maquet P, Salmon E et al. Exploratory study: radiomic biomarkers in multiple sclerosis. Quantitative neuroimaging with handcrafted and deep radiomics in neurological diseases.107."},{"key":"1046_CR95","doi-asserted-by":"publisher","first-page":"102972","DOI":"10.1016\/j.nicl.2022.102972","volume":"34","author":"N De Stefano","year":"2022","unstructured":"De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, et al. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. NeuroImage: Clin. 2022;34:102972.","journal-title":"NeuroImage: Clin"},{"issue":"3","key":"1046_CR96","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1177\/0272989X221143058","volume":"43","author":"K Chalkou","year":"2023","unstructured":"Chalkou K, Vickers AJ, Pellegrini F, Manca A, Salanti G. Decision curve analysis for personalized treatment choice between multiple options. Med Decis Making. 2023;43(3):337\u201349.","journal-title":"Med Decis Making"},{"issue":"1","key":"1046_CR97","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s41512-018-0037-2","volume":"2","author":"NR Cook","year":"2018","unstructured":"Cook NR. Quantifying the added value of new biomarkers: how and how not. Diagn Prognostic Res. 2018;2(1):14.","journal-title":"Diagn Prognostic Res"},{"issue":"7","key":"1046_CR98","doi-asserted-by":"publisher","first-page":"e048008","DOI":"10.1136\/bmjopen-2020-048008","volume":"11","author":"GS Collins","year":"2021","unstructured":"Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008.","journal-title":"BMJ Open"},{"issue":"4","key":"1046_CR99","first-page":"e240300","volume":"6","author":"AS Tejani","year":"2024","unstructured":"Tejani AS, Klontzas ME, Gatti AA, Mongan JT, Moy L, Park SH, et al. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiology: Artif Intell. 2024;6(4):e240300.","journal-title":"Radiology: Artif Intell"},{"key":"1046_CR100","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.mri.2023.09.010","volume":"104","author":"M Caranova","year":"2023","unstructured":"Caranova M, Soares JF, Batista S, Castelo-Branco M, Duarte JV. A systematic review of microstructural abnormalities in multiple sclerosis detected with NODDI and DTI models of diffusion-weighted magnetic resonance imaging. Magn Reson Imaging. 2023;104:61\u201371.","journal-title":"Magn Reson Imaging"},{"issue":"3","key":"1046_CR101","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1007\/s00330-023-10173-6","volume":"34","author":"M Gloor","year":"2024","unstructured":"Gloor M, Andelova M, Gaetano L, Papadopoulou A, Burguet Villena F, Sprenger T, et al. Longitudinal analysis of new multiple sclerosis lesions with magnetization transfer and diffusion tensor imaging. Eur Radiol. 2024;34(3):1680\u201391.","journal-title":"Eur Radiol"},{"key":"1046_CR102","doi-asserted-by":"crossref","unstructured":"York EN, Thrippleton MJ, Meijboom R, Hunt DPJ, Waldman AD. Quantitative magnetization transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis. Brain Commun. 2022;4(2).","DOI":"10.1093\/braincomms\/fcac088"},{"issue":"22","key":"1046_CR103","doi-asserted-by":"publisher","first-page":"e2965","DOI":"10.1212\/WNL.0000000000010909","volume":"95","author":"M Moccia","year":"2020","unstructured":"Moccia M, van de Pavert S, Eshaghi A, Haider L, Pichat J, Yiannakas M, et al. Pathologic correlates of the magnetization transfer ratio in multiple sclerosis. Neurology. 2020;95(22):e2965\u201376.","journal-title":"Neurology"}],"container-title":["Radiological Physics and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12194-026-01046-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12194-026-01046-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12194-026-01046-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T08:04:01Z","timestamp":1781078641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12194-026-01046-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":103,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["1046"],"URL":"https:\/\/doi.org\/10.1007\/s12194-026-01046-w","relation":{},"ISSN":["1865-0333","1865-0341"],"issn-type":[{"value":"1865-0333","type":"print"},{"value":"1865-0341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]},"assertion":[{"value":"5 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article contains no studies with human participants or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical statement"}},{"value":"All authors have read and agreed to the published material and the manuscript version.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}