Intra-fraction motion in radiotherapy is commonly modeled using deformable
image registration (DIR). However, existing methods often struggle to balance
speed and accuracy, limiting their applicability in clinical scenarios. This
study introduces a novel approach that harnesses Neural Graphics Primitives
(NGP) to optimize the displacement vector field (DVF). Our method leverages
learned primitives, processed as splats, and interpolates within space using a
shallow neural network. Uniquely, it enables self-supervised optimization at an
ultra-fast speed, negating the need for pre-training on extensive datasets and
allowing seamless adaptation to new cases. We validated this approach on the
4D-CT lung dataset DIR-lab, achieving a target registration error (TRE) of
1.15\pm1.15 mm within a remarkable time of 1.77 seconds. Notably, our method
also addresses the sliding boundary problem, a common challenge in conventional
DIR methods.

该研究提出了一种新颖的方法，利用神经图形原语（NGP）来优化位移向量场（DVF），通过浅层神经网络在空间内插值来进行自我监督优化，达到了 1.77 秒的非常快的速度，并且解决了传统 DIR 方法中常见的滑动边界问题。