Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI

• URL: http://arxiv.org/abs/2601.22990v1
• Date: Fri, 30 Jan 2026 13:56:44 GMT
• Title: Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI
• Authors: Yinsong Wang, Thomas Fletcher, Xinzhe Luo, Aine Travers Dineen, Rhodri Cusack, Chen Qin,
• Abstract summary: Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task.<n>We propose GaussianSVR, a self-supervised framework for slice-to-volume reconstruction.<n>It represents the target volume using 3D Gaussian representations to achieve high-fidelity reconstruction.
• Score: 8.241855252355386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice-to-volume reconstruction (SVR) methods are time-consuming and require multiple orthogonal stacks for reconstruction. While learning-based SVR approaches have significantly reduced the time required at the inference stage, they heavily rely on ground truth information for training, which is inaccessible in practice. To address these challenges, we propose GaussianSVR, a self-supervised framework for slice-to-volume reconstruction. GaussianSVR represents the target volume using 3D Gaussian representations to achieve high-fidelity reconstruction. It leverages a simulated forward slice acquisition model to enable self-supervised training, alleviating the need for ground-truth volumes. Furthermore, to enhance both accuracy and efficiency, we introduce a multi-resolution training strategy that jointly optimizes Gaussian parameters and spatial transformations across different resolution levels. Experiments show that GaussianSVR outperforms the baseline methods on fetal MR volumetric reconstruction. Code will be available upon acceptance.

Related papers

• Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization [6.712738779940082]
  We introduce a fast convolutional framework that fuses multiple 2D slice stacks to recover coherent 3D structure.<n>Applying to fetal brain MRI, our approach reconstructs high-quality 3D volumes in under 10s, with 1s slice registration and accuracy on par with state-of-the-art iterative SVR pipelines.
  arXiv  Detail & Related papers  (2026-01-12T13:18:49Z)
• Fast and Explicit: Slice-to-Volume Reconstruction via 3D Gaussian Primitives with Analytic Point Spread Function Modeling [16.058575857567373]
  High-resolution 3D reconstruction of the brain from motion-corrupted low-resolution 2D acquisitions is a prerequisite for accurate neurodevelopmental diagnosis.<n>In this work, we propose a shift from neural network based representations to explicit representations.<n>Our framework paves the way towards translation into clinical routine of real-time fetal 3D MRI.
  arXiv  Detail & Related papers  (2025-12-12T15:01:30Z)
• GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering [50.675710727721786]
  We propose GauSSmart, a hybrid method that bridges 2D foundational models and 3D Gaussian Splatting reconstruction.<n>Our approach integrates established 2D computer vision techniques, including convex filtering and semantic feature supervision.<n>We validate our approach across three datasets, where GauSSmart consistently outperforms existing Gaussian Splatting.
  arXiv  Detail & Related papers  (2025-10-16T03:38:26Z)
• SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction [7.268308489093152]
  We propose an unsupervised iterative SVR-SRR framework for isotropic HR volume reconstruction.<n>A decoding network embedded within a deep image prior framework is incorporated with a comprehensive image degradation model to produce the high-resolution (HR) volume.<n>Experiments conducted on large-magnitude motion-corrupted simulation data and clinical data demonstrate the superior performance of the proposed framework.
  arXiv  Detail & Related papers  (2025-05-23T04:53:59Z)
• DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
  We present DeSiRe-GS, a self-supervised gaussian splatting representation.<n>It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
  arXiv  Detail & Related papers  (2024-11-18T05:49:16Z)
• Discretized Gaussian Representation for Tomographic Reconstruction [36.64964395405005]
  Discretized Gaussian Representation (DGR) is a novel framework that reconstructs the 3D volume directly using a set of discretized Gaussian functions.<n>Fast Volume Reconstruction is a highly parallelized technique that aggregates Gaussian contributions into the voxel grid with minimal overhead.
  arXiv  Detail & Related papers  (2024-11-07T16:32:29Z)
• R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
  3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction. This paper introduces R2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
  arXiv  Detail & Related papers  (2024-05-31T08:39:02Z)
• MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo [54.00987996368157]
  We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) MVSGaussian achieves real-time rendering with better synthesis quality for each scene.
  arXiv  Detail & Related papers  (2024-05-20T17:59:30Z)
• GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting [14.937297984020821]
  We propose a novel clothed human reconstruction method called GaussianBody, based on 3D Gaussian Splatting. Applying the static 3D Gaussian Splatting model to the dynamic human reconstruction problem is non-trivial due to complicated non-rigid deformations and rich cloth details. We show that our method can achieve state-of-the-art photorealistic novel-view rendering results with high-quality details for dynamic clothed human bodies.
  arXiv  Detail & Related papers  (2024-01-18T04:48:13Z)
• Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI [9.512063866033126]
  In magnetic resonance imaging (MRI), slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices corrupted by motion. Here, we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion. Experiments on the SVR of adult and fetal brains demonstrate that our fully convolutional method is twice as accurate as previous SVR methods.
  arXiv  Detail & Related papers  (2023-12-05T19:45:44Z)
• VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction [64.09702079593372]
  VolRecon is a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF) On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction.
  arXiv  Detail & Related papers  (2022-12-15T18:59:54Z)
• GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
  Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
  arXiv  Detail & Related papers  (2022-07-18T06:01:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.