PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction

arXiv:2306.11023v2 Announce Type: replace-cross
Abstract: Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed inverse problem to obtain the desired tissue parameter maps from acquired raw data. While various learned and non-learned approaches have been proposed, the existing learned methods fail to fully exploit the prior knowledge about the underlying MR physics, i.e. the signal model and the acquisition model. In this paper, we propose PINQI, a …

abstract acquired arxiv challenge cs.cv cs.lg data eess.iv imaging lies maps measurement mri parameters physics physics-informed physics.med-ph quantitative raw raw data type while