# Magnetic resonance imaging of human post-mortem tissues with a clinical diagnosis of multiple sclerosis using inhomogeneous magnetization transfer > **NIH NIH R21** · BETH ISRAEL DEACONESS MEDICAL CENTER · 2020 · $218,750 ## Abstract PROJECT SUMMARY/ABSTRACT The National Multiple Sclerosis (MS) Society estimates that 1 million people are living with MS in the United States. The importance of magnetic resonance imaging (MRI) in diagnosis of the demyelinating neurological disease MS is evident from the McDonald Criteria guidelines. However the use of MRI in diagnosis of MS is often qualitative and analysis of images from the central nervous system is subjective. This application is dedicated to development of a robust, quantitative MRI technique, known as inhomogeneous magnetization transfer (ihMT), for future use in MS diagnosis. The ihMT MRI technique demonstrates myelin sensitivity due to its partial dependency on dipolar order, which is particularly prominent in the unique lipid bilayer structure that makes up myelin. Loss of myelin is inherent to the progression of MS, and many other neurological disorders. Demonstration of ihMT's sensitivity and specificity to myelin changes in MS would provide untold benefit not only in detection, but also for monitoring progression and testing therapies non-invasively. Indeed, preliminary study of ihMT MRI demonstrates a significant correlation with EDSS, a measure of disability. However MS is a complex disease that not only involves demyelination, but other processes like inflammation, that may confound state-of-the-art MRI techniques. We believe quantitative ihMT MRI, which can provide insight into multiple quantitative parameters associated with the microstructure of the brain and spinal cord, will be able to provide information on the underlying MS pathology in a non-invasive manner. Confirmation of the information on MS provided by quantitative ihMT requires comparison with histology, which still represents a gold standard. We aim to provide such a comparison by application of quantitative ihMT in post-mortem tissues with and without a clinical diagnosis of MS. This proposed study would serve to determine the quantitative parameters in ihMT that change in the presence of MS, and histology would confirm how those changes relate to the microstructure of central nervous system tissue. In order to demonstrate the benefit of quantitative ihMT MRI over other methods, we also plan to compare quantitative ihMT with other state-of-the-art MRI techniques that have been applied in prior studies of MS. To allow higher resolution MRI and a more informative comparison with histology, the bulk of MRI experiments will be conducted on a high field scanner. We will confirm that the information collected at the higher field strength is applicable at clinical 3T field strength by verifying any changes in quantitative ihMT parameters are demonstrable at both field strengths. The information provided by this proposed study on the relationship between quantitative ihMT and histology would be invaluable to future application of quantitative ihMT MRI for non-invasive assessment of MS in patients, as well as guiding its use in other neurological disorders. ## Key facts - **NIH application ID:** 10018119 - **Project number:** 5R21NS114546-02 - **Recipient organization:** BETH ISRAEL DEACONESS MEDICAL CENTER - **Principal Investigator:** Gopal Varma - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $218,750 - **Award type:** 5 - **Project period:** 2019-09-15 → 2023-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10018119 ## Citation > US National Institutes of Health, RePORTER application 10018119, Magnetic resonance imaging of human post-mortem tissues with a clinical diagnosis of multiple sclerosis using inhomogeneous magnetization transfer (5R21NS114546-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10018119. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*