Introduction

Methods

NeSVoR is a deep learning package for solving slice-to-volume reconstruction problems (i.e., reconstructing a 3D isotropic high-resolution volume from a set of motion-corrupted low-resolution slices) with application to fetal/neonatal MRI, which provides

1. Motion correction by mapping 2D slices to a 3D canonical space using Slice-to-Volume Registration Transformers (SVoRT).

2. Volumetric reconstruction of multiple 2D slices using implicit neural representation (NeSVoR).

Figure 1. SVoRT: an iterative Transformer for slice-to-volume registration. (a) The k-th iteration of SVoRT. (b) The detailed network architecture of the SVT module.

Figure 2. NeSVoR: A) The forward imaging model in NeSVoR. B) The architecture of the implicit neural network in NeSVoR.

Pipeline

To make our reconstruction tools more handy, we incorporate several preprocessing and downstream analysis tools in this package. The next figure shows our overall reconstruction pipeline.

Figure 3. The reconstruction pipeline.