A Physics-Driven Neural Network with Parameter Embedding for Generating Quantitative MR Maps from Weighted Images

TL;DR

This paper introduces a physics-driven neural network that embeds MRI sequence parameters to accurately synthesize quantitative MRI maps from weighted images, demonstrating superior generalization and robustness over traditional models.

Contribution

The novel integration of MRI sequence parameters via embedding into a neural network improves quantitative MRI synthesis accuracy and generalization to unseen pathologies.

Findings

Achieved PSNR > 34 dB and SSIM > 0.92 for all maps

Outperformed conventional models in accuracy and robustness

Successfully synthesized maps for unseen pathological regions

Abstract

We propose a deep learning-based approach that integrates MRI sequence parameters to improve the accuracy and generalizability of quantitative image synthesis from clinical weighted MRI. Our physics-driven neural network embeds MRI sequence parameters -- repetition time (TR), echo time (TE), and inversion time (TI) -- directly into the model via parameter embedding, enabling the network to learn the underlying physical principles of MRI signal formation. The model takes conventional T1-weighted, T2-weighted, and T2-FLAIR images as input and synthesizes T1, T2, and proton density (PD) quantitative maps. Trained on healthy brain MR images, it was evaluated on both internal and external test datasets. The proposed method achieved high performance with PSNR values exceeding 34 dB and SSIM values above 0.92 for all synthesized parameter maps. It outperformed conventional deep learning models in accuracy and robustness, including data with previously unseen brain structures and lesions. Notably, our model accurately synthesized quantitative maps for these unseen pathological regions, highlighting its superior generalization capability. Incorporating MRI sequence parameters via parameter embedding allows the neural network to better learn the physical characteristics of MR signals, significantly enhancing the performance and reliability of quantitative MRI synthesis. This method shows great potential for accelerating qMRI and improving its clinical utility.