Low Latency and Generalizable Dynamic MRI via L+S Alternating GD and Minimization

Abstract

In this work, we develop novel MRI reconstruction approaches that are accurate, fast and low-latency for a large number of dynamic MRI applications, sampling schemes and sampling rates; without any problem-specific parameter tuning. We refer to this property of a single algorithm, without parameter tuning, being accurate and fast for many settings as generalizability. Generalizability is possible only for simple (few parameter) models such as low-rank (LR) or LR plus sparse (L plus S), and for simple few parameter algorithms based on these models, which is what we develop and evaluate in this work.

13 pages, 7 figures