Rendering of 4D Ultrasound Data with Denoised Monte Carlo Path Tracing

We present a rendering system for 4D ultrasound data based on Monte Carlo path tracing, where a recurrent denoising autoencoder is trained on a large collection of images to produce noise-free images with a reduced number of samples per pixel. While the diagnostic value of photorealistic shading for 3D medical imaging has not been established definitively, the enhanced shape and depth perception allow for a more complete understanding of the data in a variety of scenarios. The dynamic nature of ultrasound data typically limits the global illumination effects that can be rendered interactively, but we demonstrated that AI-based denoising together with Monte Carlo path tracing can be used both for interactive workflows and for rendering an entire heartbeat sequence at high quality in about a minute, while also allowing for complex lighting environments. Specifically, our contribution is a model compatible with the NVIDIA OptiX interactive denoiser, which has been trained on ultrasound-specific rendering presets and data.