Events

Life at the Bottom: NMR and MRI at 6.5 mT

Moderator: Dr. Boris Epel, Ph.D., University of Chicago

About the Speaker: Dr. Matt Rosen is a physicist, tool-builder and inventor whose research bridges the spectrum from fundamental physics to applied bioimaging work in the field of MRI. He established the Low-Field MRI and Hyperpolarized Media Laboratory at the Athinoula A. Martinos Center for Biomedical Imaging to focus on the continued development of new hyperpolarization methods and MRI-based tools. The Rosen Lab focuses on new methods and tools to enable unconventional approaches to MRI scanner construction. This includes the development of new acquisition strategies for robust ultra-low magnetic field implementations of MRI focused on brain imaging. The laboratory also explores opportunities provided by hyperpolarization including in vivo Overhauser DNP, SABRE and spin-exchange optical pumping. The lab creates new quantitative strategies for the acquisition and the reconstruction of highly undersampled imaging data including neural network deep learning-based approaches such as AUTOMAP that leverage low-cost scalable-compute. Dr. Rosen co-directs the Center for Machine Learning at the Martinos Center. He is an Associate Professor of Radiology at Harvard Medical School and the Kiyomi and Ed Baird MGH Research Scholar.

About the Webinar: A promising approach to portable MRI is operation at ultra-low magnetic field where cost-effective electromagnets become practical. MRI in the ultra-low field (ULF) regime —when the magnetic field used for signal detection is below 10 mT—is inherently challenging mainly due to intrinsically low Boltzmann polarization. We will discuss signal acquisition approaches and hardware methods to improve attainable SNR in the Johnson-noise-dominated Larmor frequency of 276 kHz (6.5 mT). We will also discuss our work to reduce noise and increase attainable information per unit time using compute-based approaches that leverage low-cost GPU. These include magnetic resonance fingerprinting (MRF) to enable multiple quantitative contrasts at ULF, and the use of our neural network deep learning approach, AUTOMAP, to reconstruct highly-undersampled low SNR imaging data. In addition, we will discuss several classes of NMR and MRI experiments enabled by operation at low magnetic field, which can outperform what can be done with high-field instruments.