activity
20242026
most citedBrain age identification from diffusion MRI synergistically predicts neurodegenerative disease

3 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Generalizable field of view extension for diffusion MRI via conditional flow matching

Gaurav Rudravaram, Lianrui Zuo, Ema Topolnjak +22

Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractography and connectivity studies.…

eess.IV2025

Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding

Tianyuan Yao, Zhiyuan Li, Praitayini Kanakaraj +6

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging. It is arguably the sole noninvasive technique for examining the microstructural propertie…

cs.CV2024★ 3 cited

Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

Chenyu Gao, Michael E. Kim, Karthik Ramadass +27

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting ea…

cs.DC2024★ 1 cited

Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets

Michael E. Kim, Chenyu Gao, Karthik Ramadass +16

Proper quality control (QC) is time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorl…

q-bio.NC2024

Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites

Hanliang Xu, Nancy R. Newlin, Michael E. Kim +15

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter…