Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging

TL;DR

This paper introduces a physics-informed neural network that embeds the Bloch equation to estimate tissue parameters in MRI without requiring pre-existing training data, demonstrating promising results in phantom and cardiac imaging.

Contribution

It presents a novel PINN approach that incorporates the Bloch equation directly into the loss function for parameter estimation in MRI, eliminating the need for large training datasets.

Findings

Successfully estimates T2 parameter in MRI.

Generates physically consistent synthetic data.

Shows potential in phantom and cardiac imaging.

Abstract

Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI.