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.
@article{arxiv.2601.07519,
title = {Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization},
author = {Margherita Firenze and Sean I. Young and Clinton J. Wang and Hyuk Jin Yun and Elfar Adalsteinsson and Kiho Im and P. Ellen Grant and Polina Golland},
journal= {arXiv preprint arXiv:2601.07519},
year = {2026}
}