PHYSICS-BASED DECODING IMPROVES MAGNETIC RESONANCE FINGERPRINTING

Abstract

Magnetic Resonance Fingerprinting (MRF) is a promising paradigm to perform fast quantitative Magnetic Resonance Imaging (QMRI). However, existing MRF methods suffer from slow imaging speeds and poor generalization performance on radio frequency pulse sequences generated in various scenarios. To address these issues, we propose a novel MRI physics-informed learning approach for MRF. The proposed approach adopts a supervised encoder-decoder framework, where the encoder predicts the target tissue properties and the decoder reconstructs the inputs using the MRF physics. Specifically, the encoder embeds high-dimensional MRF time sequences into a low-dimensional tissue property space, while the decoder exploits an MRI physics model to reconstruct the input signals using the predicted tissue properties and associated MRI settings. It allows to learn better representations by integrating a fast and differentiable MRI physics model as the physics-informed regularization. The physics-based decoder improves the generalization performance and uniform stability by a considerable margin in practical out-of-distribution settings. Extensive experiments verified the effectiveness of the proposed physics-based decoding and achieved state-of-the-art performance on tissue property estimation.

1. INTRODUCTION

Quantitative Magnetic Resonance Imaging (QMRI) is used to identify tissue's intrinsic properties, such as the spin-lattice magnetic relaxation time (T1), the spin-spin magnetic relaxation time (T2), and other physical properties. Compared to conventional weighted (qualitative) MRI that focuses on tissue's contrast of brightness and darkness, QMRI reveals tissue's intrinsic properties with quantitative values and associated physical interpretations. Since different tissues are characterized by their distinct properties values, QMRI shows great potential to reduce subjectivity, with advantages in many areas including diagnosis, tissue characterization, investigation of disease pathologies, etc. [3, 30, 61] . Magnetic Resonance Fingerprinting (MRF) provides an alternative QMRI framework to achieve multi-property quantification simultaneously [43] . Given a pseudo-random radio frequency (RF) pulse sequence, a distinct magnetic response -a.k.a. fingerprint, signature, or signal evolutionfrom each specific tissue is observed and then used to predict the target tissue properties. Therefore, multi-property quantification boils down to an inverse problem that aims to infer underlying tissue properties from the magnetic responses. Various approaches have been developed to solve the MRF problem, using model-based techniques, e.g. dictionary matching (DM), compressive sensing, as well as learning-based / data-driven techniques [6, 8, 9, 11, 15, 16, 18, 25, 38, 43, 46, 47, 50, 55, 57, 58] . In spite of good performance in particular situations, they rarely take the MRI dynamics into consideration. This can cause reduced robustness and generalizability to potential data shifts occurred in practical scenarios with serious negative consequences. For example, the T1 and T2 value range and distribution are patient-specific and subject to pathological tissue types, development phase and other factors, which may cause label shift. In addition, as specific RF settings can often be applied to different situations, hospitals, and MRI instruments, MRF models are naturally expected to be able to handle such varied cases and be generalized to different RF settings. Motivated by these issues, we aim to develop a new MRF

