High performance ultrasound simulation using Monte-Carlo simulation: a GPU ray-tracing implementation

Tomás Pérez Cambet; Santiago Vitale; Ignacio Larrabide

doi:10.1117/12.2576099

3 November 2020 High performance ultrasound simulation using Monte-Carlo simulation: a GPU ray-tracing implementation

Tomás Pérez Cambet, Santiago Vitale, Ignacio Larrabide

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Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830D (2020) https://doi.org/10.1117/12.2576099
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru

Abstract

Medical ultrasound has gained relevance in medical diagnosis, procedure guidance and emergentology. It is a low cost, safe and non-invasive practice, while obtaining real-time results. Limitations on working hours and ultrasound equipment have made simulation the main alternative for training, since it eliminates the need for a real ultrasound machine and allows the modeling of a large number of case studies. Patient 11 from the 3D-IRCADb-01 CT-scans data-set was used to generate corresponding OBJ mesh files. A JSON file carries relevant information of the scene and tissues involved in it. Monte-Carlo simulation over GPU is used to improve ray tracing performance on a deterministic surface model. An improvement in ray tracing performance and throughput is achieved by the presented model. In execution cases presented, a minimum global acceleration of 30% is reached, while reducing ray tracing times by at least 80%. Monte-Carlo simulation allows a more realistic image generation than the previous deterministic model. Low memory usage gives room to implement the remaining stages of the process on GPU. A correct parametrization of tissues is required in order to achieve improved image quality.

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Tomás Pérez Cambet, Santiago Vitale, and Ignacio Larrabide "High performance ultrasound simulation using Monte-Carlo simulation: a GPU ray-tracing implementation", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830D (3 November 2020); https://doi.org/10.1117/12.2576099

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