Department of Radiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany.
Department of Neuroradiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany.
Rofo. 2020 Sep;192(9):847-853. doi: 10.1055/a-1167-8402. Epub 2020 Jul 8.
MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools were developed to analyze imaging data in MS. This review gives an overview of the most important applications with special emphasis on artificial intelligence (AI).
Relevant studies were identified through a literature search in recognized databases, and through parsing the references in studies found this way. Literature published as of November 2019 was included with a special focus on recent studies from 2018 and 2019.
There are a number of studies which focus on optimizing lesion visualization and lesion segmentation. Some of these studies accomplished these tasks with high accuracy, enabling a reproducible quantitative analysis of lesion loads. Some studies took a radiomics approach and aimed at predicting clinical endpoints such as the conversion from a clinically isolated syndrome to definite MS. Moreover, recent studies investigated synthetic imaging, i. e. imaging data that is not measured during an MR scan but generated by a computer algorithm to optimize the contrast between MS lesions and brain parenchyma.
Computer-based image analysis and AI are hot topics in imaging MS. Some applications are ready for use in clinical routine. A major challenge for the future is to improve prediction of expected disease courses and thereby helping to find optimal treatment decisions on an individual level. With technical improvements, more questions arise about the integration of new tools into the radiological workflow.
· Computer algorithms have a growing impact on analyzing MR imaging in MS.. · Artificial intelligence is more and more commonly employed in such computer tools.. · Applications include lesion segmentation, prediction of clinical parameters and image synthesizing..
· Eichinger P, Zimmer C, Wiestler B. AI in Radiology: Where are we today in Multiple Sclerosis Imaging?. Fortschr Röntgenstr 2020; 192: 847 - 853.
磁共振成像(MRI)是多发性硬化症(MS)患者管理的重要组成部分。这在初始诊断以及评估 MS 的临床病程中都是如此。近年来,越来越多的计算机工具被开发出来用于分析 MS 的影像学数据。本综述概述了最重要的应用,特别强调了人工智能(AI)。
通过在公认的数据库中进行文献检索,并通过解析以这种方式找到的研究参考文献,确定了相关研究。纳入截至 2019 年 11 月发表的文献,并特别关注 2018 年和 2019 年的最新研究。
有许多研究专注于优化病变可视化和病变分割。其中一些研究以高精度完成这些任务,从而能够对病变负荷进行可重复的定量分析。一些研究采用了放射组学方法,旨在预测临床终点,例如从临床孤立综合征到明确 MS 的转化。此外,最近的研究还探讨了合成成像,即不是在 MRI 扫描期间测量而是由计算机算法生成以优化 MS 病变与脑实质之间对比度的成像数据。
基于计算机的图像分析和 AI 是 MS 成像的热门话题。一些应用已经准备好用于临床常规。未来的主要挑战是提高对预期疾病过程的预测能力,从而帮助在个体层面找到最佳的治疗决策。随着技术的进步,更多的问题出现在将新工具集成到放射科工作流程中。
·计算机算法对分析 MS 的 MRI 具有越来越大的影响。·人工智能越来越多地应用于此类计算机工具中。·应用包括病变分割、预测临床参数和图像合成。