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.

磁共振成像中切片到体积重建是从被运动干扰的 2D 切片堆叠中计算重建未知 3D 磁共振体积，该论文提出了一种能够克服之前方法的缺点，在极端切片间运动的情况下产生最先进的重建，通过将 SVR 形式化为单堆叠运动估计任务，并训练一个完全卷积网络来预测给定切片堆叠的运动堆叠，作为预测运动的副产品产生三维重建。大量实验证明，我们的完全卷积方法比之前的 SVR 方法精度提高了一倍。