Neural Graphics Primitives-based Deformable Image Registration for
  On-the-fly Motion Extraction
Xia Li Fabian Zhang Muheng Li Damien Weber Antony Lomax Joachim Buhmann Ye Zhang
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.