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

描述(申请人提供)：多发性硬化症(MS)，一种困扰中枢神经系统的神经退行性疾病，以大脑和脊髓的病变形成和萎缩为特征。据报道，萎缩发生在疾病的早期，并随着疾病的进展而增加，在不同的皮质和皮质下区域，反映了髓鞘、轴突和神经细胞体的广泛丢失。过去十年发表的研究表明，磁共振成像(MRI)的最新进展在检测、可视化和量化MS疾病的发生和发展方面取得了很大进展。为了使诊断和评估疾病进展的这些进展继续并系统地改变多发性硬化症的临床评估，必须开发准确、自动化和可靠地检测多发性硬化症病变和量化脑萎缩的技术，以提高在临床环境中的利用率。这项建议的主要目标是开发一种人工免疫分类(AIC)技术，用于准确、自动和稳健的MRI数据分析，以达到MS病变检测和局部脑萎缩的量化的目的。拟议的AIC技术用于定量测量MS疾病的影响和进展，旨在通过一种通用和统一的方法来解决当前评估MS的挑战，该方法依赖人工免疫功能来准确识别大脑中的不同组织类别。在赠款的第一阶段，拟议的AIC技术的原型将被开发出来，并在一项试点研究中进行评估，该研究涉及多发性硬化症患者和对照的真实核磁共振数据。此外，评估将涉及不同水平的多发性硬化症疾病负担、噪声和强度不均匀的模拟MRI数据。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病变负荷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：拟议的项目在检测多发性硬化症(MS)引起的大脑异常的准确性和量化方面所产生的增强，将使人们能够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多发性硬化症的临床试验的规划。

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.