PhyMRI-SR：邁向物理感知的MRI影像超解析

摘要

磁振造影超解析對於提升診斷可近性至關重要，然而多數方法將其視為從固定低解析度輸入到高解析度目標的確定性映射。這種做法忽略了磁振造影成像物理的一項關鍵特性：空間解析度與訊號雜訊比本質上相互耦合，使得任何給定的低解析度掃描僅是在不同成像權衡下眾多可能實現之一。我們將磁振造影超解析重新構想為一個物理感知的重建問題，其目標在於辨識出最優的解析度-訊噪比配置，進而透過超解析獲得高品質磁振造影結果。此架構的一項關鍵含義在於，磁振造影解析度將成為動態而非固定的。為處理此類解析度異質性輸入，我們將二維高斯潑濺法（2D GS）改編應用於磁振造影，將重建問題表述為基於座標、無關解析度的渲染問題。為進一步提升保真度，我們引入三項創新：（1）先驗感知高斯表示法，結合「解剖結構先驗」用於組織特異性核初始化，以及「成像系統先驗」透過共變異數字典捕捉硬體特性；（2）物理約束訊號建模方案，可預測內在組織參數（質子密度ρ與有效弛豫率R₂），並透過支配物理方程式合成訊號強度，確保生物物理上合理的對比；（3）元學習框架，透過在模擬資料上預訓練並適應真實世界條件，減輕成對資料稀缺問題。在動態解析度資料集與標準基準上的大量實驗證明，我們的方法達到了最先進的表現，突顯其在臨床部署上的強大潛力。

English

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.