555 citations · 573 across the 13 of their papers we have counts for
6 papers · 1 filter
LGGS: Longitudinal MRI Reconstruction via Guided Group Sparsity
Nikola Janjušević, Hersh Chandarana, Yao Wang +1
Serial MRI is routinely performed in current clinical practice for disease surveillance and applications such as MR-guided radiotherapy. However, long MRI scan times increase costs…
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…
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…
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI
Haoyang Pei, Nikola Janjuvsevic, Renqing Luo +6
Deep learning has demonstrated strong potential for MRI reconstruction. However, conventional supervised learning requires high-quality, high-SNR references for network training, w…
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…