dorsal/arxiv
View SchemaFast 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 |
|---|---|
| Categories | |
| ArXiv ID | 2601.07519vv1 |
| URL | https://arxiv.org/abs/2601.07519 |
| 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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"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.",
"arxiv_id": "2601.07519",
"authors": [
"Margherita Firenze",
"Sean I. Young",
"Clinton J. Wang",
"Hyuk Jin Yun",
"Elfar Adalsteinsson",
"Kiho Im",
"P. Ellen Grant",
"Polina Golland"
],
"categories": [
"eess.IV",
"cs.CV"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Fast Multi-Stack Slice-to-Volume Reconstruction via Multi-Scale Unrolled Optimization",
"url": "https://arxiv.org/abs/2601.07519",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
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"variant": "snapshot-2026-01-17",
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