4 citations · 10 across the 14 of their papers we have counts for
7 papers · 1 filter
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…