Multi-Model Detection and Quantification of Multiple Sclerosis in MR Imaging

描述（由申请人提供）：多发性硬化症（MS）是一种折磨中枢神经系统的神经退行性疾病，以脑和脊髓病变形成和萎缩为特征。据报道，萎缩发生在疾病早期，并随着疾病进展在各种皮层和皮层下区域增加，反映髓磷脂、轴突和神经细胞体的广泛损失。在过去十年中发表的研究表明，磁共振成像（MRI）的最新进展在MS疾病的发生和进展的检测、可视化和量化方面取得了巨大进展。为了在诊断和评估疾病进展方面取得这些进展，并系统地改变MS的临床评估，必须开发准确、自动化和可靠的MS病变检测和脑萎缩量化技术，以提高临床应用。本课题的主要目的是开发一种人工免疫分类（AIC）技术，用于准确、自动化和鲁棒的MRI数据分析，用于MS病变检测和区域脑萎缩量化。提出的AIC技术用于定量测量多发性硬化症的影响和进展，旨在通过一种通用和统一的方法来解决当前评估多发性硬化症的挑战，该方法依赖于人工免疫功能来准确识别大脑中不同的组织类别。在第一阶段拨款期间，将开发拟议AIC技术的原型，并在涉及MS患者和对照组的真实MRI数据的试点研究中进行评估。此外，评估将涉及模拟MRI数据在不同水平的MS疾病负担，噪音和强度不均匀性。第一阶段将提供拟议的AIC技术的概念验证，并证明其在评估MS病变负担和量化白质和灰质的区域准确性方面的实际可行性。公共卫生相关性：拟议项目在多发性硬化症（MS）引起的大脑异常的检测准确性和量化方面的改进将使人们更好地了解疾病的过程、进展和对大脑不同区域的不同影响，这将有助于改进针对MS的临床试验计划。

DESCRIPTION (provided by applicant): Multiple sclerosis (MS), a neurodegenerative disease that afflicts the central nervous system, is characterized by lesion formation and atrophy of the brain and spinal cord. Atrophy was reported to occur early in the disease and to increase with the disease progression in various cortical and sub-cortical regions, reflecting widespread loss of myelin, axons and neural cell bodies. Published studies in the past decade have demonstrated that recent advances in Magnetic Resonance Imaging (MRI) have exhibited great progress in the detection, visualization and quantification of the onset and progression of MS disease. In order for these advances in the diagnosis and assessment of the progression of the disease to continue and systematically change the clinical evaluation of MS, techniques for accurate, automated, and robust detection of MS lesions and quantification of brain atrophy must be developed to enable increased utilization in clinical settings. The main objective of this proposal is to develop an artificial immune classification (AIC) technique for accurate, automated and robust MRI data analysis for the purpose of MS lesion detection and quantification of regional brain atrophy. The proposed AIC technique for quantitative measurement of the effect and progression of MS disease aims to tackle current challenges in assessing MS through a generic and unified approach that relies on artificial immune functions to enable accurate identification of different tissue classes in the brain. During phase I of the grant, a prototype of the proposed AIC technique will be developed and evaluated in a pilot study involving real MRI data of MS patients and controls. In addition, the evaluation will involve simulated MRI data at varying levels of MS disease burden, noise, and intensity in-homogeneity. Phase I will provide a proof-of-concept of the proposed AIC technique as well as demonstrate its practical feasibility for assessment of MS lesion burden and regional accuracy for quantifying white matter and gray matter. PUBLIC HEALTH RELEVANCE: The enhancements resulting from the proposed project in detection accuracy and quantification of brain abnormalities due to multiple sclerosis (MS) would enable better understanding of the disease processes, progression and varying effects on different regions of the brain, which would allow improved planning of clinical trials focusing on MS.