M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstruction

TL;DR

M-Gaussian is presented, adapting 3D Gaussian Splatting to multi-stack MRI reconstruction, representing the first successful adaptation of 3D Gaussian Splatting to multi-stack MRI reconstruction.

Abstract

Magnetic Resonance Imaging (MRI) is a crucial non-invasive imaging modality. In routine clinical practice, multi-stack thick-slice acquisitions are widely used to reduce scan time and motion sensitivity, particularly in challenging scenarios such as fetal brain imaging. However, the resulting severe through-plane anisotropy compromises volumetric analysis and downstream quantitative assessment, necessitating robust reconstruction of isotropic high-resolution volumes. Implicit neural representation methods, while achieving high quality, suffer from computational inefficiency due to complex network structures. We present M-Gaussian, adapting 3D Gaussian Splatting to MRI reconstruction. Our contributions include: (1) Magnetic Gaussian primitives with physics-consistent volumetric rendering, (2) neural residual field for high-frequency detail refinement, and (3) multi-resolution progressive training. Our method achieves an optimal balance between quality and speed. On the FeTA dataset, M-Gaussian achieves 40.31 dB PSNR while being 14 times faster, representing the first successful adaptation of 3D Gaussian Splatting to multi-stack MRI reconstruction.

Figures (9)

• Figure 1: Overview of the M-Gaussian framework. (a) The training pipeline integrates Gaussian rendering with a Neural Residual Field (NRF) refinement module. (b) Point cloud construction is performed via multi-stack registration and devoxelization to initialize the Gaussians. (c) The inference stage generates the final volume through Gaussian sampling and aggregation at the target resolution.
• Figure 2: Comparison between original 3D Gaussian Splatting primitives and the proposed Magnetic Gaussian primitives. The original 3DGS utilizes view-dependent spherical harmonic coefficients for color representation, whereas M-Gaussian employs a single intensity value to model tissue-specific MRI signal properties, which significantly reduces memory overhead.
• Figure 3: Qualitative comparison of slice-to-volume MRI reconstruction methods. Each row shows results from a different dataset (FeTA, FaBiAN, HCP). The Input column displays one representative stack from the multi-stack acquisition. Our M-Gaussian method produces reconstructions with sharper anatomical boundaries and fewer artifacts compared to baseline methods.
• Figure 4: Qualitative ablation results on Progressive resolution (P.R.) training and SSIM loss. Progressive resolution training stabilizes convergence, while the inclusion of SSIM loss significantly enhances the preservation of structural integrity and contrast. The effect is particularly significant on FeTA dataset.
• Figure 5: Qualitative ablation results on Neural Residual Field (NRF). The NRF suppresses noise and refines high-frequency details, which is prominent on the HCP dataset.