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Graphics Processing Unit-Based Bioheat Simulation to Facilitate Rapid Decision Making Associated with Cryosurgery

Robert Keelan¹, Hong Zhang¹, Kenji Shimada¹

¹Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.

Technology in Cancer Research & Treatment

|May 6, 2015

Summary

Researchers developed a faster cryosurgery simulation using graphics processing units (GPUs). This accelerates runtime for medical training tools, enabling quicker analysis of temperature fields and frozen region contours for improved prostate cryosurgery training.

Keywords:

GPU bioheat cryosurgery planning simulation training

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Area of Science:

Computational physics
Medical simulation
Biomedical engineering

Background:

Cryosurgery simulations are crucial for developing effective medical training tools.
Prostate cryosurgery is a key area for developing and testing these simulation models.
Rapid simulation capabilities are essential for informed decision-making in medical training.

Purpose of the Study:

To implement an efficient numerical technique for cryosurgery simulations on a graphics processing unit (GPU).
To accelerate simulation runtime for enhanced medical training tools.
To assess the feasibility of GPU-based computation for phase-change problems in cryosurgery.

Main Methods:

Utilized C++ accelerated massive parallelism for GPU-based computation.
Developed a numerical technique for cryosurgery simulations.
Compared GPU simulation performance against multicore central processing unit (CPU) implementations.

Main Results:

Achieved significant runtime acceleration for cryosurgery simulations.
Demonstrated 3-fold acceleration on a laptop and 13-fold acceleration on a gaming computer compared to CPU.
Validated the application of GPU computing to phase-change problems in cryosurgery.

Conclusions:

GPU-based computation offers a viable and efficient method for accelerating cryosurgery simulations.
The developed technique enhances the potential for creating advanced computerized training tools for cryosurgery.
This approach addresses the unique optimization requirements of cryosurgery and phase-change modeling.