555 citations · 573 across the 13 of their papers we have counts for
15 papers
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…
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…
Masked and Predictive Self-Supervised Pretraining for Brain MRI: Effects of Objective Design on Disease Detection
Esra Ergün, Hersh Chandarana, Dan Sodickson +2
MRI self-supervised learning (SSL) has largely emphasized segmentation and dense prediction, while controlled studies of how alternative pretraining objectives shape transferable r…
GRASP MRI: A Decade of Innovation from Bench to Bedside
Li Feng, Kai Tobias Block, Hersh Chandarana +1
GRASP (Golden-angle RAdial Sparse Parallel) MRI has emerged as one of the most influential motion-robust dynamic MRI frameworks over the past decade. By combining continuous golden…
Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation
Li Feng, Hersh Chandarana
The inherently slow acquisition speed of MRI makes abdominal imaging highly sensitive to respiratory motion artifacts. Since the early days of MRI, the development of respiratory m…
Context-aware deep learning using individualized prior information reduces false positives in disease risk prediction and longitudinal health assessment
Lavanya Umapathy, Patricia M Johnson, Tarun Dutt +4
Temporal context in medicine is valuable in assessing key changes in patient health over time. We developed a machine learning framework to integrate diverse context from prior vis…
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…
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
Radhika Tibrewala, Tarun Dutt, Angela Tong +8
The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) v…
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
Raghav Singhal, Mukund Sudarshan, Anish Mahishi +7
Early detection of many life-threatening diseases (e.g., prostate and breast cancer) within at-risk population can improve clinical outcomes and reduce cost of care. While numerous…
Optimization of MR Fingerprinting for Free-Breathing Quantitative Abdominal Imaging
Max H. C. van Riel, Zidan Yu, Shota Hodono +4
In this work, we propose a free-breathing magnetic resonance fingerprinting method that can be used to obtain -robust quantitative maps of the abdomen in a clinically accept…
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Jure Zbontar, Florian Knoll, Anuroop Sriram +24
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applicati…