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arXiv · 2601.07519

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

Margherita Firenze·Sean I. Young·Clinton J. Wang·Hyuk Jin Yun·Elfar Adalsteinsson·Kiho Im·P. Ellen Grant·Polina Golland
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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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Margherita Firenze, Sean I. Young, Clinton J. Wang, Hyuk Jin Yun, Elfar Adalsteinsson, Kiho Im, P. Ellen Grant, Polina Golland. 2026-01-12. Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization. https://arxiv.org/abs/2601.07519

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