5 citations · 23 across the 27 of their papers we have counts for
30 papers
Separating mixtures of single-fiber orientation distributions for increased sensitivity to crossing white matter fibers
Adam M. Saunders, Gaurav Rudravaram, Michael E. Kim +10
Intro: Diffusion MRI allows for in vivo probing of brain tissue architecture via micron-level diffusion aggregated at the millimeter scale, but crossing white matter fibers decreas…
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.…
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…
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…
Evaluation of neuroCombat and deep learning harmonization for multi-site magnetic resonance neuroimaging in youth with prenatal alcohol exposure
Chloe Scholten, Elyssa M. McMaster, Adam M. Saunders +8
In cases of prevalent diseases and disorders, such as Prenatal Alcohol Exposure (PAE), multi-site data collection allows for increased study samples. However, multi-site studies in…
Harmonization mitigates diffusion MRI scanner effects in infancy: insights from the HEALthy Brain and Childhood Development (HBCD) study
Elyssa M. McMaster, Gaurav Rudravaram, Michael E. Kim +17
The HEALthy Brain and Childhood Development (HBCD) Study is an ongoing longitudinal initiative to understand population-level brain maturation; however, large-scale studies must ov…
Personalized White Matter Bundle Segmentation for Early Childhood
Elyssa M. McMaster, Michael E. Kim, Nancy R. Newlin +12
White matter segmentation methods from diffusion magnetic resonance imaging range from streamline clustering-based approaches to bundle mask delineation, but none have proposed a p…
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…
Quality assurance of the Federal Interagency Traumatic Brain Injury Research (FITBIR) database for multi-site MRI analysis
Adam M. Saunders, Michael E. Kim, Gaurav Rudravaram +8
The Federal Interagency Traumatic Brain Injury Research (FITBIR) database is a centralized data repository for traumatic brain injury (TBI) research. It includes over 45,529 magnet…
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…
Fully Differentiable dMRI Streamline Propagation in PyTorch
Jongyeon Yoon, Elyssa M. McMaster, Michael E. Kim +4
Diffusion MRI (dMRI) provides a distinctive means to probe the microstructural architecture of living tissue, facilitating applications such as brain connectivity analysis, modelin…
Phenotype discovery of traumatic brain injury segmentations from heterogeneous multi-site data
Adam M. Saunders, Michael E. Kim, Gaurav Rudravaram +8
Traumatic brain injury (TBI) is intrinsically heterogeneous, and typical clinical outcome measures like the Glasgow Coma Scale complicate this diversity. The large variability in s…
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…
Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD
Aravind R. Krishnan, Yihao Liu, Kaiwen Xu +9
Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue motion in COPD patients. Deforma…
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…
Sensitivity of quantitative diffusion MRI tractography and microstructure to anisotropic spatial sampling
Elyssa M. McMaster, Nancy R. Newlin, Chloe Cho +10
Purpose: Diffusion weighted MRI (dMRI) and its models of neural structure provide insight into human brain organization and variations in white matter. A recent study by McMaster,…
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…
Harmonized connectome resampling for variance in voxel sizes
Elyssa M. McMaster, Nancy R. Newlin, Gaurav Rudravaram +10
To date, there has been no comprehensive study characterizing the effect of diffusion-weighted magnetic resonance imaging voxel resolution on the resulting connectome for high reso…