Meeting Banner
Abstract #3472

Self-supervised and physics informed deep learning model for prediction of multiple tissue parameters from MR Fingerprinting data

Richard James Adams1, Yong Chen1, Pew-Thian Yap2, and Dan Ma1
1Case Western Reserve University, Cleveland, OH, United States, 2University of North Carolina, Chapel Hill, NC, United States


Magnetic resonance fingerprinting (MRF) simultaneously quantifies multiple tissue properties. Deep learning accelerates MRF’s tissue mapping time; however, previous deep learning MRF models are supervised, requiring ground truths of tissue property maps. It is challenging to acquire quality reference maps, especially as the number of tissue parameters increases. We propose a self-supervised model informed by the physical model of the MRF acquisition without requiring ground truth references. Additionally, we construct a forward model that directly estimates the gradients of the Bloch equations. This approach is flexible for modeling MRF sequences with pseudo-randomized sequence designs where an analytical model is not available.

This abstract and the presentation materials are available to members only; a login is required.

Join Here