37 citations · 39 across the 5 of their papers we have counts for
6 papers
Distortion-Free High-Resolution PROPELLER-DWI: Technical Advances and Initial Demonstration for the Prostate
Jingjia Chen, Kun Zhou, Haoyang Pei +7
Purpose: To develop and evaluate an integrated acquisition and reconstruction framework for distortion-free, high-resolution prostate diffusion-weighted imaging (DWI). Methods: PRO…
Free-Breathing PDFF and R2* Quantification at 0.55T: Reduced Respiratory Motion Sensitivity and a Time-Resolved 4D MRI Approach
Jeffery Wong, Jingjia Chen, Ding Xia +5
Purpose: Free-breathing multi-echo MRI enables hepatic PDFF/R2* quantification without breath-holds, but respiratory motion can affect R2* estimation through motion-induced field v…
Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lungs at 0.55T
Jingjia Chen, Haoyang Pei, Christoph Maier +6
Purpose: This study aims to improve 0.55T T2-weighted PROPELLER lung MRI through a self-supervised joint reconstruction and denoising model. Methods: T2-weighted 0.55T lung MRI dat…
Multisession Longitudinal Dynamic MRI Incorporating Patient-Specific Prior Image Information Across Time
Jingjia Chen, Hersh Chandarana, Daniel K. Sodickson +1
Serial Magnetic Resonance Imaging (MRI) exams are often performed in clinical practice, offering shared anatomical and motion information across imaging sessions. However, existing…
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning
Nikola Janjušević, Jingjia Chen, Luke Ginocchio +5
Purpose: This work proposes a novel self-supervised noise-adaptive image denoising framework, called Repetition to Repetition (Rep2Rep) learning, for low-field (<1T) MRI applicatio…
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
Jie Feng, Ruimin Feng, Qing Wu +3
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. H…