Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI

Sean I. Young, Yael Balbastre, Bruce Fischl, Polina Golland, Juan Eugenio Iglesias; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 11535-11545

In magnetic resonance imaging (MRI) slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices corrupted by motion. While promising current SVR methods require multiple slice stacks for accurate 3D reconstruction leading to long scans and limiting their use in time-sensitive applications such as fetal fMRI. Here we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion. Inspired by the recent success of single-view depth estimation methods we formulate SVR as a single-stack motion estimation task and train a fully convolutional network to predict a motion stack for a given slice stack producing a 3D reconstruction as a byproduct of the predicted motion. Extensive experiments on the SVR of adult and fetal brains demonstrate that our fully convolutional method is twice as accurate as previous SVR methods. Our code is available at github.com/seannz/svr.