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Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution

Shuting Liu, Lei Zhang, Wei Huang, Zhao Zhang, Zizhou Wang

TL;DR

A zero-shot MRI SR framework using explicit Gaussian representation to balance data requirements and efficiency is proposed, and a brick-based order-independent rasterization scheme enables highly parallel 3D computation, lowering training and inference costs.

Abstract

High-resolution Magnetic Resonance Imaging (MRI) is vital for clinical diagnosis but limited by long acquisition times and motion artifacts. Super-resolution (SR) reconstructs low-resolution scans into high-resolution images, yet existing methods are mutually constrained: paired-data methods achieve efficiency only by relying on costly aligned datasets, while implicit neural representation approaches avoid such data needs at the expense of heavy computation. We propose a zero-shot MRI SR framework using explicit Gaussian representation to balance data requirements and efficiency. MRI-tailored Gaussian parameters embed tissue physical properties, reducing learnable parameters while preserving MR signal fidelity. A physics-grounded volume rendering strategy models MRI signal formation via normalized Gaussian aggregation. Additionally, a brick-based order-independent rasterization scheme enables highly parallel 3D computation, lowering training and inference costs. Experiments on two public MRI datasets show superior reconstruction quality and efficiency, demonstrating the method's potential for clinical MRI SR.

Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution

TL;DR

A zero-shot MRI SR framework using explicit Gaussian representation to balance data requirements and efficiency is proposed, and a brick-based order-independent rasterization scheme enables highly parallel 3D computation, lowering training and inference costs.

Abstract

High-resolution Magnetic Resonance Imaging (MRI) is vital for clinical diagnosis but limited by long acquisition times and motion artifacts. Super-resolution (SR) reconstructs low-resolution scans into high-resolution images, yet existing methods are mutually constrained: paired-data methods achieve efficiency only by relying on costly aligned datasets, while implicit neural representation approaches avoid such data needs at the expense of heavy computation. We propose a zero-shot MRI SR framework using explicit Gaussian representation to balance data requirements and efficiency. MRI-tailored Gaussian parameters embed tissue physical properties, reducing learnable parameters while preserving MR signal fidelity. A physics-grounded volume rendering strategy models MRI signal formation via normalized Gaussian aggregation. Additionally, a brick-based order-independent rasterization scheme enables highly parallel 3D computation, lowering training and inference costs. Experiments on two public MRI datasets show superior reconstruction quality and efficiency, demonstrating the method's potential for clinical MRI SR.
Paper Structure (11 sections, 6 equations, 4 figures, 3 tables)

This paper contains 11 sections, 6 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Preliminaries and Motivation
  3. Method
  4. MRI-Tailored Gaussian Parameters
  5. Physics-Grounded Volume Rendering
  6. Brick-Based Order-Independent Rasterizer
  7. Experiments
  8. Dataset
  9. Comparison with Prior Work
  10. Ablation and Efficiency Analysis
  11. Conclusion

Figures (4)

  • Figure 1: The overall workflow of our method. 3D Gaussians are initialized solely from the coordinates of the low-resolution volume. During rendering, 3DGS relies on spherical harmonics (SH)-based view-dependent $\alpha$-blending to produce RGB color, whereas our approach uses physics-grounded volume rendering to render voxel intensity. In the spatial distribution stage, 3DGS requires depth sorting followed by 2D splatting, while our method aggregates Gaussian contributions and performs brick-based rendering directly in 3D space to generate high-resolution MRI.
  • Figure 2: Illustration of the key MRI signal formation processes that challenge the original 3DGS framework and motivate our MRI-tailored Gaussian model. In MRI, protons first align with and precess in the static magnetic field $B_0$ (a), then an RF pulse tips them into the transverse plane where they dephase and relax (b), producing the time‐dependent signal used to reconstruct the image.
  • Figure 3: Visual comparison of the proposed method against state‑of‑the‑art approaches on two public datasets. Left column: Full reconstructed images. Top-right: Cropped region (indicated by a green box on the ground truth). Bottom-right: Pixel-wise error maps between reconstructed patches and corresponding HR reference patches.
  • Figure 4: Radar chart of zero‑shot MRI SR methods (NeRF, CuNeRF, Ours) on MSD simpson2019msd at 2× (left) and 4× (right) upsampling. Metrics: PSNR/SSIM for quality and training time, inference time, peak VRAM for resources.