15 papers
Solving a Nonlinear Blind Inverse Problem for Tagged MRI with Physics and Deep Generative Priors
Zhangxing Bian, Shuwen Wei, Samuel W. Remedios +4
Tagged MRI enables tracking internal tissue motion non-invasively. It encodes motion by modulating anatomy with periodic tags, which deform along with tissue. However, the entangle…
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…
Diffusion-Driven Generation of Minimally Preprocessed Brain MRI
Samuel W. Remedios, Aaron Carass, Jerry L. Prince +1
The purpose of this study is to present and compare three denoising diffusion probabilistic models (DDPMs) that generate 3D -weighted MRI human brain images. Three DDPMs were…
MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis
Jinwei Zhang, Lianrui Zuo, Yihao Liu +10
In multiple sclerosis, lesions interfere with automated magnetic resonance imaging analyses such as brain parcellation and deformable registration, while lesion segmentation models…
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…