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

Neural graphics primitives are faster and achieve higher quality when their
neural networks are augmented by spatial data structures that hold trainable
features arranged in a grid. However, existing feature grids either come with a
large memory footprint (dense or factorized grids, trees, and hash tables) or
slow performance (index learning and vector quantization). In this paper, we
show that a hash table with learned probes has neither disadvantage, resulting
in a favorable combination of size and speed. Inference is faster than unprobed
hash tables at equal quality while training is only 1.2-2.6x slower,
significantly outperforming prior index learning approaches. We arrive at this
formulation by casting all feature grids into a common framework: they each
correspond to a lookup function that indexes into a table of feature vectors.
In this framework, the lookup functions of existing data structures can be
combined by simple arithmetic combinations of their indices, resulting in
Pareto optimal compression and speed.

通过将所有特征网格转化为一个共同的框架，我们通过简单的索引运算将现有数据结构的查找函数组合在一起，实现了帕累托最优压缩和速度，从而表明具有学习探针的哈希表在大小和速度上具有优势。

学习哈希探测的紧凑神经图形原语

Compact Neural Graphics Primitives with Learned Hash Probing

Neural graphics primitives, parameterized by fully connected neural networks,
can be costly to train and evaluate. We reduce this cost with a versatile new
input encoding that permits the use of a smaller network without sacrificing
quality, thus significantly reducing the number of floating point and memory
access operations: a small neural network is augmented by a multiresolution
hash table of trainable feature vectors whose values are optimized through
stochastic gradient descent. The multiresolution structure allows the network
to disambiguate hash collisions, making for a simple architecture that is
trivial to parallelize on modern GPUs. We leverage this parallelism by
implementing the whole system using fully-fused CUDA kernels with a focus on
minimizing wasted bandwidth and compute operations. We achieve a combined
speedup of several orders of magnitude, enabling training of high-quality
neural graphics primitives in a matter of seconds, and rendering in tens of
milliseconds at a resolution of ${1920\!\times\!1080}$.

通过多分辨率哈希表和 CUDA 核心的并行处理，我们能够在几秒钟内训练和呈现高品质的神经图形原语，极大地减少了浮点运算和内存访问操作。