Solomon's MRI Lab - Advanced Imaging 2 Magnetic Resonance
We are the MRI research group led by Dr. Eddy Solomon. We combine physics, engineering, and artificial intelligence (AI) to impact high-stake real-world problem settings in medical diagnostics, with a particular focus on Magnetic Resonance Imaging (MRI).
Our work aims to make MRI faster, more accurate, and more accessible to children and elderly patients by developing novel approaches and methodology in close collaboration with practitioners and domain experts. In this context, the main focus of our group includes:
- Computational MRI, acquisition strategies, and image reconstruction aided by deep learning.
- Fast MRI and motion-robust MRI under free-breathing conditions.
- MR-compatible external physiological sensors, including pilot-tone systems, ultra-wideband radar, and accelerometers.
- Advanced diffusion MRI methods to improve diagnostic reliability in cancer imaging.
- Infrared imaging technology for cancer detection.
đŁ News
| Jun 30, 2026 | New Publication in Nature Communications, 2026: We are pleased to announce the publication of our latest research, âDynamic breast MRI with Flexible Temporal Resolution Aided by Deep Learningâ in Nature Communications. In this work, we present ELITE, a deep-learning reconstruction framework that combines artificial intelligence with mathematical modeling to produce fast dynamic MRI for breast cancer imaging at up to one frame per second. |
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| Jun 30, 2026 | New Publication in medRxiv, 2026: We are pleased to announce the publication of our latest research, âSoftware-defined Radar for MRI Motion Correction: A versatile, vendor-independent Platformâ in medRxiv. This work introduces a vendor-independent platform for MRI motion correction using software-defined radar, expanding our toolkit for robust imaging in the presence of motion. |
| Mar 13, 2026 | MR Education lecture: Hosted by the British & Irish Chapter, I had the privilege to give this weekâs talk titled âMotion Correctionâ. |
| Feb 26, 2026 | Paper submitted to MICCAI 2026: Our latest work titled âBRISKNet: Breast Rapid Imaging via Self-Supervised Kineticsâ led by our collaborators Rachel Gordon and Anna Woodard from the University of Chicago (Department of Computer Science), introduces a new approach to accelerating breast MRI through self-supervised learning of dynamic imaging kinetics. |
| Feb 11, 2026 | Magnetic Resonance Seminar at Weizmann Institute: It was great pleasure to visit the Weizmann Institute of Science for an exciting Magnetic Resonance Seminar, where I presented our latest work on dynamic breast MRI with flexible temporal resolution enabled by deep learning. |
đ Selected publications
Latest publication
Navigator motionâresolved MR fingerprinting using implicit neural representation: Feasibility for freeâbreathing threeâdimensional wholeâliver multiparametric mapping
Most cited publication
Training a neural network for Gibbs and noise removal in diffusion MRI