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。

基于神经图形特征的可变形图像配准用于即时运动提取

Neural Graphics Primitives-based Deformable Image Registration for  On-the-fly Motion Extraction

Even though simultaneous optimization of similarity metrics represents a
standard procedure in the field of semantic segmentation, surprisingly, this
does not hold true for image registration. To close this unexpected gap in the
literature, we investigate in a complex multi-modal 3D setting whether
simultaneous optimization of registration metrics, here implemented by means of
primitive summation, can benefit image registration. We evaluate two
challenging datasets containing collections of pre- to post-operative and pre-
to intra-operative Magnetic Resonance Imaging (MRI) of glioma. Employing the
proposed optimization we demonstrate improved registration accuracy in terms of
Target Registration Error (TRE) on expert neuroradiologists' landmark
annotations.

本文研究基于先前的相似度度量方法是否适用于图像配准的问题，探究在多模态 3D 环境下，通过选择基本求和作为度量优化手段是否能够提高 MRI 图像配准的准确性。通过 2 个预先和手术中的胶质母细胞瘤病例，依据神经放射科医生的地标标记，证明了本文提出的优化方法对图像配准精度提升的效果。