PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution

2026-07-07

AI Summary

This work addresses the limitation of conventional MRI super-resolution methods, which overlook the intrinsic physical coupling between resolution and signal-to-noise ratio by treating the task as a deterministic mapping. The authors propose a physics-aware dynamic-resolution reconstruction framework that leverages coordinate-driven, resolution-agnostic 2D Gaussian splatting rendering to generate high-quality images. Key innovations include a Gaussian representation integrating anatomical and imaging-system priors, physics-based modeling of tissue parameters, prior-guided initialization of Gaussian kernels with a covariance dictionary, and a meta-learning pretraining strategy to mitigate the scarcity of paired training data. The method achieves state-of-the-art performance on both dynamic-resolution datasets and standard benchmarks, demonstrating substantial potential for clinical translation.

Abstract

Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs. We rethink MRI super-resolution as a physics-aware reconstruction problem, in which the goal is to identify the optimal resolution-SNR configuration and then super-resolve it to obtain high-quality MRI results. A key implication of this formulation is that MRI resolution becomes dynamic rather than fixed. To handle such resolution-heterogeneous inputs, we adapt 2D Gaussian Splatting (2D GS) to MRI by formulating reconstruction as a coordinate-based, resolution-agnostic rendering problem. To further enhance fidelity, we introduce three innovations: (1) a prior-aware Gaussian representation that combines an Anatomical Structure Prior for tissue-specific kernel initialization with an Imaging System Prior that captures hardware characteristics via a covariance dictionary; (2) a physics-constrained signal modeling scheme that predicts intrinsic tissue parameters (proton density rho and effective relaxation rate R2) and synthesizes intensities through governing physical equations, ensuring biophysically plausible contrast; and (3) a meta-learning framework that alleviates paired-data scarcity by pretraining on simulated data and adapting to real-world conditions. Extensive experiments on dynamic-resolution datasets and standard benchmarks demonstrate that our method achieves state-of-the-art performance, highlighting its strong potential for clinical deployment.

Problem

• MRI super-resolution
• resolution-SNR coupling
• physics-aware reconstruction
• dynamic resolution
• acquisition physics

Innovation

• physics-aware reconstruction
• Gaussian Splatting
• anatomical prior
• biophysical modeling
• meta-learning