Computational imaging biomarkers of multiple sclerosis

摘要 多发性硬化症(MS)是一种慢性中枢神经系统炎症性疾病， 认知和运动障碍，影响到近50万美国人和全球250万人。在……里面 活体MRI可以检测出该病标志性的脑白质病变及其随时间的变化，具有显著的 比临床对疾病活动的评估更敏感。此外，许多研究表明， 序列MRI评估，多发性硬化症患者各种脑结构萎缩加剧的速度快于非多发性硬化症患者 健康对照，并与残疾衡量标准相关。因此，能够可靠而高效地 脑白质损伤的形态计量学特征、各种神经解剖结构及其变化 随着时间的推移，直接来自体内的MRI将在诊断疾病、跟踪 进展和评估治疗。 虽然许多用于从MS患者的MR扫描中分割白质病变的自动工具已经被 开发出来的，这些通常只针对特定的研究协议进行调整，并不解决 MS患者的脑萎缩模式的特征，已知病变的存在干扰了 萎缩估计。此外，计算神经成像在多发性硬化症中的努力几乎已经集中在 仅展示人口水平的统计关联，而不是预测模型 同时结合所有信息源，计算出个体最敏感的生物标志物 病人。 为了解决这些限制，本项目的目标是(1)开发和验证扫描仪的自动化工具- 脑白质损伤的神经解剖学背景下的自适应分割；(2)开发和部署 用于在个体患者级别预测残疾的空间正则化模型；以及(3)推广， 在纵向设置中验证并应用建议的细分和预测工具。成功者 该项目的完成将在多发性硬化症中产生一组更好地关联的计算成像生物标志物 具有比目前可用的方法更好的临床观察；公开可用的软件工具 通过多种成像硬件和协议分割多发性硬化症患者的纵向扫描； 更详细地描述疾病进展背后的形态和时间动力学 以及多发性硬化症的残疾累积。

Abstract Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system that causes significant cognitive and motor deficits and affects nearly half a million Americans and 2.5 million individuals worldwide. In vivo MRI can detect the disease’s hallmark white matter lesions and their changes over time with a significantly higher sensitivity than clinical assessment of disease activity. Furthermore, numerous studies have shown that the atrophy accrual in various brain structures, assessed from serial MRI, is faster in patients with MS than in healthy controls, and correlates with measures of disability. Therefore, the ability to reliably and efficiently characterize the morphometry of white matter lesions, various neuroanatomical structures, and their changes over time directly from in vivo MRI would be of great potential value for diagnosing disease, tracking progression, and evaluating treatment. While many automatic tools for segmenting white matter lesions from MR scans of MS patients have been developed, these are typically tuned for specific research protocols only, and do not address the problem of characterizing brain atrophy patterns in MS patients, where the presence of lesions is known to interfere with atrophy estimation. Furthermore, computational neuroimaging efforts in MS have been focused almost exclusively on demonstrating statistical associations on population levels, rather than on prediction models that combine all sources of information simultaneously to compute the most sensitive biomarker in individual patients. In order to address these limitations, this project aims to (1) develop and validate automated tools for scanner- adaptive segmentation of white matter lesions within their neuroanatomical context; (2) develop and deploy spatially regularized models for predicting disability at the level of the individual patient; and (3) generalize, validate, and apply the proposed segmentation and prediction tools in longitudinal settings. The successful completion of this project will result in a set of computational imaging biomarkers in MS that correlate better with clinical observation than currently available methods; publicly available software tools for robustly segmenting longitudinal scans of MS patients across a wide range of imaging hardware and protocols; and a more detailed characterization of the morphological and temporal dynamics underlying disease progression and accumulation of disability in MS.