Computational imaging biomarkers of multiple sclerosis

多发性硬化症的计算成像生物标志物

• 批准号: 10187669
• 负责人: Koen Van Leemput
• 金额: $ 37.79万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10187669
• 关键词: Address; Affect; American; Atrophic; Benchmarking; Biological Markers; Brain; Central Nervous System Diseases; Chronic; Clinical; Clinical Research; Clinical Trials; Clinical assessments; Cognitive deficits; Computational Technique; Counseling; Data; Disease; Disease Management; Disease Marker; Disease Progression; Future; Goals; Image; Individual; Inflammatory; Label; Lesion; MRI Scans; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Methods; Modeling; Morphology; Multiple Sclerosis; Outcome; Patients; Pattern; Performance; Population; Procedures; Prognosis; Protocols documentation; Research; Scanning; Shapes; Software Tools; Source; Structure; Testing; Time; Time Study; base; brain morphology; cerebral atrophy; clinical development; clinical practice; clinical predictors; computer studies; computerized tools; disability; disease diagnosis; gray matter; high risk; imaging biomarker; improved; in vivo; individual patient; morphometry; motor deficit; multiple sclerosis patient; neural network; neuroimaging; novel; predictive modeling; prospective; tool; white matter

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.

抽象的 多发性硬化症（MS）是中枢神经系统的慢性炎症性疾病，会引起明显的 认知和运动在全球范围内定义并影响了近50万美国人和250万人。在 体内MRI可以检测到该疾病的标志性白质病变及其随着时间的变化 比疾病活动的临床评估更高的灵敏度。此外，许多研究表明 通过串行MRI评估的各种大脑结构中的萎缩症在MS患者中比在 健康控制，与残疾措施相关。因此，可靠有效的能力 表征白质病变的形态计量学，各种神经解剖结构及其变化 随着时间的流逝，直接来自体内MRI的诊断疾病将具有巨大的潜在价值，跟踪 进展和评估治疗。 虽然许多用于从MS患者的MR扫描中细分白质病变的自动工具是 开发的，这些通常仅针对特定的研究协议进行调整，并且不会解决 表征MS患者中脑萎缩模式的表征，其中已知病变的存在会干扰 萎缩估计。此外，MS中的计算神经影像学工作几乎被重点 专门展示人口水平的统计关联，而不是预测模型 将所有信息来源组合起来，只是为了计算个体中最敏感的生物标志物 患者。 为了解决这些局限 在神经解剖学的环境下对白质病变的自适应分割； （2）开发和部署 通过空间调节的模型，用于预测个别患者水平的残疾； （3）概括， 验证并将提议的分割和预测工具应用于纵向设置。成功 该项目的完成将导致MS中的一组计算成像生物标志物，以更好地相关联 比目前可用的方法进行临床观察；公开可用的软件工具 将MS患者的纵向扫描分割为各种成像硬件和协议的纵向扫描；和 疾病进展的形态和临时动力学的更详细的表征 MS中残疾的积累。