Computational imaging biomarkers of multiple sclerosis

点击翻译按钮获取中文摘要

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.