Yogesh Rathi, Ph.D,  Brigham and Women’s Hospital, Harvard Medical School

Abstract: MRI data acquired from multiple scanners is affected by several site specific effects such as the vendor specific reconstruction algorithm, the sensitivity of the receiver coils, as well as differences in acquisition parameters. This is especially true for diffusion MRI data. Consequently, direct pooling of data from different scanners can result in dramatically decreased statistical power in detecting subtle changes in the white/gray matter tissue microstructure. Our experimental work shows that these scanner effects are non-linear and vary across different parts of the brain. In this talk, I will present our algorithm on harmonizing dMRI data acquired from multiple scanners for retrospective as well as prospective multi-site studies. In particular, key advantages/disadvantages of this method compared to the standard methods of statistical covariates or meta-analysis will be discussed. I will also talk about our ongoing efforts on harmonizing 30,000 subjects from ABCD, HCP and HBN databases, which will enable very large-scale data analysis using any type of dMRI model.

Brief bio:  Yogesh Rathi is an Associate Professor at the Brigham and Women’s Hospital, Harvard Medical School. Dr. Rathi’s research interests lie in developing smart imaging techniques to understand brain structure and function. His current research focus is on

1). Ultra-high resolution diffusion imaging in a clinically feasible scan time combining acquisition and reconstruction;

2). Harmonizing multi-site diffusion MRI data from 30,000 subjects;

3). estimating tissue microstructure from biophysical and stochastic models of diffusion, and

4). time-series analysis for understanding functional connectivity using fMRI data.

His clinical research focus includes using and developing sophisticated tractography algorithms for precise targeting of deep-brain stimulation (DBS) and transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD), Parkinson’s and major depressive disorder (MDD). His broad research focus is in the areas of signal and image-processing, statistics, control theory, machine learning, computer vision and related applications to solve inverse problems in medical imaging.

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Speaker

Yogesh Rathi, Ph.D,  Brigham and Women’s Hospital, Harvard Medical School