8 papers
Masked and Predictive Self-Supervised Foundation Models for 3D Brain MRI
Esra Ergün, Hersh Chandarana, Dan Sodickson +1
Self-supervised foundation models have shown strong promise in medical imaging. However, existing MRI foundation-model studies have primarily emphasized segmentation and dense pred…
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…
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…
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…
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…