In this blog post, I will demonstrate how to use a GPU in order to accelerate the ray tracing code developed in previous blog posts. Specifically in this blog post I will be using Numba’s CUDA extension to write a CUDA kernel for the ray tracing code. Numba has support for parallel programming using both AMD and Nvidia GPUs, although this post will focus on development for a Nvidia GPU.
In my first blog post, I explained how to perform X-Ray imaging. One problem with a pure Python forward model is it’s slow. Therefore, I am going to introduce Numba in this blog post a way to accelerate Python code.
This post will review the concepts behind medical imaging and the forward model used in X-ray imaging. Additionally, this tutorial will explain the development of the forward model in Python.