4 citations · 10 across the 14 of their papers we have counts for
16 papers
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.…
Identity-Duplication Auditing in National-Scale Neuroimaging Repositories
Jiheng Li, Michael E. Kim, Trent M. Schwartz +20
National-scale magnetic resonance imaging (MRI) repositories increasingly integrate data from different studies and institutions. However, subject identifiers assigned to study par…
Bayesian Variable Selection for High-Dimensional Predictors with Missing Psychometric Outcomes
Zongyue Teng, Shujie Ma, Timothy J. Hohman +2
High-dimensional, multimodal predictors and partially observed multivariate outcomes are common in psychometric research. However, existing regularization methods often do not acco…
SliceBridge: Context-Consistent Repair of Corrupted Slice Intervals in T1-Weighted MRI
Jiheng Li, Michael E. Kim, Trent M. Schwartz +8
Structural magnetic resonance imaging (MRI) images are sometimes corrupted over a contiguous set of slices, where acquisition, motion, hardware, or reconstruction effects leave a s…
GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning
Vishnu M. Bashyam, Guray Erus, Junhao Wen +29
Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep le…
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…
Characterizing Continuous and Discrete Hybrid Latent Spaces for Structural Connectomes
Gaurav Rudravaram, Lianrui Zuo, Adam M. Saunders +12
Structural connectomes are detailed graphs that map how different brain regions are physically connected, offering critical insight into aging, cognition, and neurodegenerative dis…
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…
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…
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…