Conventional MRI is a valuable tool for the diagnosis of MS and to monitor disease progression. However, imaging findings are not well correlated with clinical outcomes, likely due to limited specificity of lesions to MS pathology, as well as an the inability to detect lesions in normal appearing brain tissue. Additionally, conventional MR metrics (T2w/FLAIR lesions) are insufficient to specify changes occurring at the macromolecular level (i.e., demyelination, remyelination, gliosis, and axonal loss). Although advance MRI techniques such as myelin water fraction (MWF) imaging, magnetization transfer ratio (MTR), macromolecular tissue volume (MTV), and diffusion tensor imaging (DTI) are being used to improve our understanding of the underlying pathophysiological process in MS, they suffer from poor reproducibility across different sites and different scanners. Accurate, reproducible imaging biomarkers are needed to understand the underlying biophysical processes behind the progression of disability, which, in turn, can enable improved therapies to stop and reverse disease progression.
Our group is focusing on using advanced quantitative MRI (qMRI) techniques in a multi-modal approach to improve the accurate quantification of basic MRI properties as a direct surrogate of tissue integrity in the voxel. This advance has the advantage of reproducibility across scanners of different strengths and the ability to detect change on an individual basis, leading to the development of more effective therapies to stop progression and improved prognostic tools to help clinicians personalize disease management.
Le Hua, MD
Le Hua, MD receives research support from the Eric and Sheila Samson Foundation.
Karthik Sreenivasan receives research support from Keep Memory Alive Young Scientist Award
A new algebraic method for quantitative proton density mapping using multi-channel coil data. Cordes D, Yang Z, Zhuang X, Sreenivasan K, Mishra V, Hua LH. Med Image Anal. 2017 Aug;40:154-171. Published online 2017 Jun 23. doi: 10.1016/j.media.2017.06.007. PMID: 28668358.
Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Mezer A.A, Yeatman J., Stikov N., Kay K., Cho N.M., Dougherty R.F., Perry M.L., Parvizi J., Hua L.H., Butts-Pauly K., Wandell B. Nature Medicine. 2013;19(12):1667-1672.doi:10.1038/nm.3390 PMID: 24185694