Kim Minjeong, Jewells Valerie
Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC.
Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill NC.
Semin Ultrasound CT MR. 2020 Jun;41(3):309-318. doi: 10.1053/j.sult.2020.02.005. Epub 2020 Feb 29.
The purpose of this paper is to serve as a template for greater understanding for the practicing radiologist about key steps to perform multimodality computer analysis of MRI images, specifically in multiple sclerosis patients. With this understanding, radiologists will be better equipped about how best to process and analyze MRI imaging data and obtain accurate quantitative information for MS patient evaluation. A secondary intent of this article is to improve radiologist understanding of how artificial intelligence will be employed in the future for better patient stratification, and for evaluation of response to therapy in both clinical care and drug trials.
本文的目的是为执业放射科医生提供一个模板，以更好地理解对MRI图像进行多模态计算机分析的关键步骤，特别是针对多发性硬化症患者。有了这种理解，放射科医生将更有能力了解如何最好地处理和分析MRI成像数据，并为MS患者评估获得准确的定量信息。本文的第二个目的是提高放射科医生对人工智能在未来如何用于更好地对患者进行分层，以及在临床护理和药物试验中评估治疗反应的理解。
