Kaloian Petkov
ABSTRACT
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.
Supplemental Material
Available for Download
- Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, Christoph Schied, Marco Salvi, Aaron Lefohn, Derek Nowrouzezahrai, and Timo Aila. 2017. Interactive Reconstruction of Monte Carlo Image Sequences Using a Recurrent Denoising Autoencoder. ACM Trans. Graph. 36, 4 (2017). https://doi.org/10.1145/3072959.3073601Google Scholar
Digital Library
- Evelyn Dappa, Kai Higashigaito, Jürgen Fornaro, Sebastian Leschka, Simon Wildermuth, and Hatem Alkadhi. 2016. Cinematic rendering –- an alternative to volume rendering for 3D computed tomography imaging. Insights into Imaging 7, 6 (2016), 849–856. https://doi.org/10.1007/s13244-016-0518-1Google ScholarCross Ref
- Thomas Kroes, Frits H. Post, and Charl P. Botha. 2012. Exposure Render: An Interactive Photo-Realistic Volume Rendering Framework. PLOS ONE 7, 7 (2012), 1–10. https://doi.org/10.1371/journal.pone.0038586Google ScholarCross Ref
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