Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization

• URL: http://arxiv.org/abs/2601.07519v1
• Date: Mon, 12 Jan 2026 13:18:49 GMT
• Title: Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization
• Authors: Margherita Firenze, Sean I. Young, Clinton J. Wang, Hyuk Jin Yun, Elfar Adalsteinsson, Kiho Im, P. Ellen Grant, Polina Golland,
• Abstract summary: 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.
• Score: 6.712738779940082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fully convolutional networks have become the backbone of modern medical imaging due to their ability to learn multi-scale representations and perform end-to-end inference. Yet their potential for slice-to-volume reconstruction (SVR), the task of jointly estimating 3D anatomy and slice poses from misaligned 2D acquisitions, remains underexplored. We introduce a fast convolutional framework that fuses multiple orthogonal 2D slice stacks to recover coherent 3D structure and refines slice alignment through lightweight model-based optimization. Applied 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, offering more than speedup. The framework uses non-rigid displacement fields to represent transformations, generalizing to other SVR problems like fetal body and placental MRI. Additionally, the fast inference time paves the way for real-time, scanner-side volumetric feedback during MRI acquisition.

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