mlsanityData to 14 Oct 2026

Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI

arXiv 2312.03102 ↗First posted 5 Dec 2023eess.IV cs.CV cs.LGcited by 10

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Abstract (arXiv)

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. While promising, current SVR methods require multiple slice stacks for accurate 3D reconstruction, leading to long scans and limiting their use in time-sensitive applications such as fetal fMRI. 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. Inspired by the recent success of single-view depth estimation methods, we formulate SVR as a single-stack motion estimation task and train a fully convolutional network to predict a motion stack for a given slice stack, producing a 3D reconstruction as a byproduct of the predicted motion. Extensive experiments on the SVR of adult and fetal brains demonstrate that our fully convolutional method is twice as accurate as previous SVR methods. Our code is available at github.com/seannz/svr.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 28 of its 76 references that are in the map. First posted on arXiv 5 Dec 2023.

Builds on

28 of 76 references.

References · 28

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16 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  2. 2025Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representationscited by 4
  3. 2025DiffUS: Differentiable Ultrasound Rendering from Volumetric Imagingcited by 2
  4. 2025Variational volume reconstruction with the Deep Ritz Methodcited by 2
  5. 2025From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstructioncited by 1
  6. 2025GriDiT: Factorized Grid-Based Diffusion for Efficient Long Image Sequence Generationcited by 1
  7. 2026cSVR: Convolutional Slice-to-Volume Reconstructioncited by 1
  8. 2026Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimizationcited by 1
  9. 2025Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mambacited by 0
  10. 2026Information Theoretic Perspective on Representation Learningcited by 0