Speaker
Description
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, including new clinical use cases.
