GaLA-SVR: Feed-Forward 3D Gaussian Framework with Physics-Aware Latent Aggregation for Slice-to-Volume Reconstruction

Reconstructing coherent 3D volumes from sparse, motion-corrupted MRI slices requires accurate motion estimation and faithful integration of incomplete observations. Existing learning-based SVR methods often reconstruct an intermediate volume assembled from motion-corrected slice intensities for subsequent refinement, rather than leveraging semantically rich slice features across stacks. This can limit their ability to exploit complementary observations from different views. In sparsely observed regions, refinement needs to hallucinate missing structures from incomplete volume intensity information. To address this, we propose GaLA-SVR, a feed-forward 3D Gaussian framework with physics-aware Latent Aggregation for Slice-to-Volume Reconstruction. GaLA-SVR bypasses existing intermediate volume reconstruction, with its core consisting of a physics-aware latent aggregation scheme and a dense patch-wise correspondence-based motion estimator. First, we propose a novel Point Spread Function (PSF)-aware feature aggregation scheme to learn a comprehensive 3D latent representation from 2D slice features, maintaining structural completeness even in sparsely observed or unobserved regions, which is then decoded into anisotropic 3D Gaussians for high-fidelity volumetric rendering. To achieve the precise spatial alignment essential for feature aggregation, we reformulate slice motion estimation as a dense patch-wise correspondence problem, facilitated by a novel Intra- and Inter-Slice Attention (IISA) block. Experiments across three MRI datasets demonstrate that GaLA-SVR achieves SOTA volumetric reconstruction quality with accurate slice motion estimation. Code will be released upon acceptance.