3 citations · 8 across the 6 of their papers we have counts for
7 papers
Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI
Ruimin Feng, Wanyu Bian, Albert Jang +2
Clinical MRI routinely acquires multiple contrast-weighted images of the same anatomy for complementary tissue characterization. However, current accelerated MRI methods typically…
MC BTS: simultaneously resolving magnetization transfer effect and relaxation for multiple components
Albert Jang, Hyungseok Jang, Nian Wang +2
We propose a signal acquisition and modeling framework for multi-component tissue quantification that encompasses transmit field inhomogeneity, multi-component relaxation and magne…
OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation
Xingxin He, Aurora Rofena, Ruimin Feng +4
Magnetic Resonance Imaging (MRI) is indispensable in clinical practice but remains constrained by fragmented, multi-stage workflows encompassing acquisition, reconstruction, segmen…
Accelerating multiparametric quantitative MRI using self-supervised scan-specific implicit neural representation with model reinforcement
Ruimin Feng, Albert Jang, Xingxin He +1
Purpose: To develop a self-supervised scan-specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). Methods: We propose REFINE-MORE…
Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning
Wanyu Bian, Albert Jang, Fang Liu
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep l…
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
Wanyu Bian, Albert Jang, Fang Liu
This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. Our method is applied to multi-coil MRI and quanti…
Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement
Wanyu Bian, Albert Jang, Fang Liu
This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a…