Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
This enables faster and more accurate MRI scans for time-sensitive applications like fetal fMRI, though it is an incremental improvement over existing slice-to-volume reconstruction techniques.
The paper tackles the problem of reconstructing 3D MRI volumes from single stacks of 2D slices corrupted by motion, achieving state-of-the-art reconstructions with twice the accuracy of previous methods.
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.