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.

翻译：在磁共振成像（MRI）中，切片到体积重建（SVR）是指从受运动伪影污染的二维切片堆栈中，通过计算重建未知三维磁共振体积的方法。尽管前景广阔，但当前的SVR方法需要多个切片堆栈才能实现精确的三维重建，这导致扫描时间过长，限制了其在胎儿功能磁共振成像等时间敏感型应用中的使用。本文提出了一种克服先前工作缺点的SVR方法，能够在极端层间运动情况下实现最先进的重建效果。受单视图深度估计方法近期成功的启发，我们将SVR形式化为单堆栈运动估计任务，并训练一个全卷积网络来预测给定切片堆栈的运动堆栈，从而通过预测运动的副产品生成三维重建。在对成人和胎儿脑部进行SVR的大量实验中表明，我们的全卷积方法比先前SVR方法的精度提高了一倍。我们的代码可在github.com/seannz/svr获取。