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

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.