论文标题
快速自动差异化的数字重建X光片，用于解决术中成像中的反问题
Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging
论文作者
论文摘要
在术前设置中，使用了数字重建的X光片（DRR）来解决诸如切片到体积注册和3D重建之类的反问题。在术中成像中，DRR的实用性受到实时生成它们以及支持依赖重复DRR合成的优化程序的挑战的限制。尽管通过算法改进和GPU实现加速了DRR的生成，但基于DRR的优化仍然很慢，因为大多数DRR发电机都没有提供有关成像参数的梯度的直接方法。为了使DRR与基于梯度的优化和深度学习框架互操作，我们重新制定了Siddon的方法，Siddon的方法是DRR生成中使用的最流行的射线追踪算法，作为一系列矢量化的张量操作。我们在Pytorch中实现了Siddon方法的矢量化版本，利用了库的强大自动分化引擎，使该DRR发电机相对于其参数完全可区分。此外，使用GPU加速张量计算使我们的矢量实现实现了与CUDA和C ++中实现的最新DRR发电机相同的渲染速度。我们在切片到体积注册的背景下说明了所得的方法。此外，我们的模拟表明，在最佳解决方案附近，切片到体积注册问题的损失景观是凸的，基于梯度的注册有望比盛行的无梯度优化策略更快。所提出的DRR发电机使快速的计算机视觉算法能够在微创过程中支持图像指导。我们的实施公开可在https://github.com/v715/diffdrr上获得。
The use of digitally reconstructed radiographs (DRRs) to solve inverse problems such as slice-to-volume registration and 3D reconstruction is well-studied in preoperative settings. In intraoperative imaging, the utility of DRRs is limited by the challenges in generating them in real-time and supporting optimization procedures that rely on repeated DRR synthesis. While immense progress has been made in accelerating the generation of DRRs through algorithmic refinements and GPU implementations, DRR-based optimization remains slow because most DRR generators do not offer a straightforward way to obtain gradients with respect to the imaging parameters. To make DRRs interoperable with gradient-based optimization and deep learning frameworks, we have reformulated Siddon's method, the most popular ray-tracing algorithm used in DRR generation, as a series of vectorized tensor operations. We implemented this vectorized version of Siddon's method in PyTorch, taking advantage of the library's strong automatic differentiation engine to make this DRR generator fully differentiable with respect to its parameters. Additionally, using GPU-accelerated tensor computation enables our vectorized implementation to achieve rendering speeds equivalent to state-of-the-art DRR generators implemented in CUDA and C++. We illustrate the resulting method in the context of slice-to-volume registration. Moreover, our simulations suggest that the loss landscapes for the slice-to-volume registration problem are convex in the neighborhood of the optimal solution, and gradient-based registration promises a much faster solution than prevailing gradient-free optimization strategies. The proposed DRR generator enables fast computer vision algorithms to support image guidance in minimally invasive procedures. Our implementation is publically available at https://github.com/v715/DiffDRR.
