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20232026
most citedPredicting Age from White Matter Diffusivity with Residual Learning

5 citations · 20 across the 17 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

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…

eess.IV2024★ 3 cited

Comparison and calibration of MP2RAGE quantitative T1 values to multi-TI inversion recovery T1 values

Adam M. Saunders, Michael E. Kim, Chenyu Gao +6

While typical qualitative T1-weighted magnetic resonance images reflect scanner and protocol differences, quantitative T1 mapping aims to measure T1 independent of these effects. C…

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…

cs.CV2024★ 4 cited

Field-of-View Extension for Brain Diffusion MRI via Deep Generative Models

Chenyu Gao, Shunxing Bao, Michael Kim +13

Purpose: In diffusion MRI (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field-of-view (F…

eess.IV2024★ 1 cited

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI

Tian Yu, Yunhe Li, Michael E. Kim +8

Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relat…

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…