Welcome to Solomon MRI Lab
Faster, Smarter, and More Accessible Medical Imaging
Our team combines expertise in physics, engineering, and deep learning to advance medical imaging, with a particular focus on Magnetic Resonance Imaging (MRI). We develop novel MRI technologies that translate cutting-edge algorithms into meaningful clinical impact, improving both the speed and quality of imaging. By making MRI faster, more reliable, and more accessible, we aim to enhance patient care across a wide range of applications, including brain, breast, and abdominal imaging.
Our Research
Computational MRI
Novel sampling and reconstruction methods: from accelerated k-space acquisition to diagnostic-quality image.
Motion-Robust MRI
Motion-robust MRI for free breathing, reliable imaging.
Advanced MRI Hardware
MR-compatible sensing and external tracking beyond conventional MRI.
Quantitative Diffusion MRI
Advanced diffusion MRI for probing tissue microstructure and cellular dynamics.
Latest News
New Publication in Nature Communications, 2026
We are pleased to announce the publication of our latest research, “Dynamic breast MRI with Flexible Temporal Resolution Aided by Deep Learning” 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.
New Publication in medRxiv, 2026
We are pleased to announce the publication of our latest research, “Software-defined Radar for MRI Motion Correction: A versatile, vendor-independent Platform” 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.
MR Education lecture
Hosted by the British & Irish Chapter, I had the privilege to give this week’s talk titled “Motion Correction”.
Paper submitted to MICCAI 2026
Our latest work titled “BRISKNet: Breast Rapid Imaging via Self-Supervised Kinetics” 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.
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.
Intreseted to join us?
We are always looking for motivated students to join our team. If you are passionate about medical imaging and AI, let's connect.
Our Collaborators
