7 citations · 26 across the 22 of their papers we have counts for
22 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…
Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution
Samuel W. Remedios, Zhangxing Bian, Shuwen Wei +3
Diffusion models are the current state-of-the-art for solving inverse problems in imaging. Their impressive generative capability allows them to approximate sampling from a prior d…
CATNUS: Coordinate-Aware Thalamic Nuclei Segmentation Using T1-Weighted MRI
Anqi Feng, Zhangxing Bian, Samuel W. Remedios +6
Accurate segmentation of thalamic nuclei from magnetic resonance images is important due to the distinct roles of these nuclei in overall brain function and to their differential i…
MedForj: An open, large-scale foundational generative prior for high-resolution 3D brain MRI
Samuel W. Remedios, Aaron Carass, Jerry L. Prince +1
This work introduces MedForj, a suite of 3D foundational generative priors based on diffusion models. The MedForj models were trained on 1~mm isotropic 3D -weighted…
Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution
Anqi Feng, Zhangxing Bian, Samuel W. Remedios +5
Accurate thalamic nuclei segmentation is crucial for understanding neurological diseases, brain functions, and guiding clinical interventions. However, the optimal inputs for segme…
UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey +9
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation,…