5 citations · 20 across the 17 of their papers we have counts for
17 papers
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…
Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling
Gaurav Rudravaram, Lianrui Zuo, Karthik Ramadass +17
Acquisition differences across sites, scanners, and protocols in dMRI introduce variability that complicates structural connectome analysis. This motivates deep learning models tha…
MetaVoxel: Joint Diffusion Modeling of Imaging and Clinical Metadata
Yihao Liu, Chenyu Gao, Lianrui Zuo +9
Modern deep learning methods have achieved impressive results across tasks from disease classification, estimating continuous biomarkers, to generating realistic medical images. Mo…
Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging
Lucas W. Remedios, Chloe Cho, Trent M. Schwartz +11
Purpose: Understanding how the pancreas changes is critical for detecting deviations in type 2 diabetes and other pancreatic disease. We measure pancreas size and shape using morph…
Data-Driven Abdominal Phenotypes of Type 2 Diabetes in Lean, Overweight, and Obese Cohorts
Lucas W. Remedios, Chloe Cho, Trent M. Schwartz +10
Purpose: Although elevated BMI is a well-known risk factor for type 2 diabetes, the disease's presence in some lean adults and absence in others with obesity suggests that detailed…
Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels
Aravind R. Krishnan, Thomas Z. Li, Lucas W. Remedios +12
Reconstruction kernels in computed tomography (CT) affect spatial resolution and noise characteristics, introducing systematic variability in quantitative imaging measurements such…
Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential
Chenyu Gao, Kaiwen Xu, Michael E. Kim +11
Defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provok…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation
Aravind R. Krishnan, Kaiwen Xu, Thomas Li +9
The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency in reconstruction kernels is important as the underlying CT textur…