activity
20232026
most citedPredicting Age from White Matter Diffusivity with Residual Learning

5 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV2026

Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging

Michael E. Kim, Chenyu Gao, Karthik Ramadass +17

Purpose: Diffusion MRI (dMRI) provides a diverse set of quantitative measures and derived datatypes to assess white matter microstructure and macrostructure. Coupled with the incre…

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…

cs.CV2024★ 1 cited

Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI

Zhiyuan Li, Chenyu Gao, Praitayini Kanakaraj +13

An incomplete field-of-view (FOV) in diffusion magnetic resonance imaging (dMRI) can severely hinder the volumetric and bundle analyses of whole-brain white matter connectivity. Al…

cs.DC2024★ 1 cited

Scalable, reproducible, and cost-effective processing of large-scale medical imaging datasets

Michael E. Kim, Karthik Ramadass, Chenyu Gao +11

Curating, processing, and combining large-scale medical imaging datasets from national studies is a non-trivial task due to the intense computation and data throughput required, va…

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…

eess.IV2023★ 5 cited

Predicting Age from White Matter Diffusivity with Residual Learning

Chenyu Gao, Michael E. Kim, Ho Hin Lee +17

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimat…