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

TL;DR

This paper presents a fast, convolutional framework for slice-to-volume reconstruction in medical imaging, achieving high-quality 3D reconstructions in seconds and enabling real-time feedback during MRI scans.

Contribution

It introduces a novel multi-scale unrolled optimization method that fuses multiple 2D slice stacks for rapid, accurate 3D reconstruction, extending to various fetal MRI applications.

Findings

Reconstructs fetal brain MRI in under 10 seconds.

Achieves slice registration in about 1 second.

Maintains accuracy comparable to state-of-the-art methods.

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.