Two-dimensional (2D) freehand ultrasonography is one of the most commonly
used medical imaging modalities, particularly in obstetrics and gynaecology.
However, it only captures 2D cross-sectional views of inherently 3D anatomies,
losing valuable contextual information. As an alternative to requiring costly
and complex 3D ultrasound scanners, 3D volumes can be constructed from 2D scans
using machine learning. However this usually requires long computational time.
Here, we propose RapidVol: a neural representation framework to speed up
slice-to-volume ultrasound reconstruction. We use tensor-rank decomposition, to
decompose the typical 3D volume into sets of tri-planes, and store those
instead, as well as a small neural network. A set of 2D ultrasound scans, with
their ground truth (or estimated) 3D position and orientation (pose) is all
that is required to form a complete 3D reconstruction. Reconstructions are
formed from real fetal brain scans, and then evaluated by requesting novel
cross-sectional views. When compared to prior approaches based on fully
implicit representation (e.g. neural radiance fields), our method is over 3x
quicker, 46% more accurate, and if given inaccurate poses is more robust.
Further speed-up is also possible by reconstructing from a structural prior
rather than from scratch.

提出了 RapidVol：一种神经表示框架用于加快切片到体积的超声重建，并通过使用张量秩分解将典型的三维体积分解为三面板的集合，再用一个小型神经网络来存储，只需一组带有三维位置和方向（姿态）的二维超声扫描就可以进行完整的三维重建。与基于完全隐式表示的先前方法相比（如神经辐射场），我们的方法速度提高了 3 倍以上，准确性提高了 46%，而且在给出不准确的姿态时具有更强的鲁棒性。可以通过从结构先验而不是从头开始进行重建来进一步加快速度。