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GPU computing in medical physics: a review.

Guillem Pratx¹, Lei Xing

¹Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305, USA. pratx@stanford.edu

Medical Physics

|July 23, 2011

Summary

This summary is machine-generated.

Graphics processing units (GPUs) accelerate medical physics computations. This review covers GPU principles, optimization, and applications in image reconstruction, dose calculation, and image processing.

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Area of Science:

Medical Physics
High-Performance Computing

Background:

Graphics Processing Units (GPUs) offer parallel processing capabilities.
Medical physics applications often involve data-parallel tasks suitable for GPUs.
GPUs can significantly reduce computation times for complex problems.

Purpose of the Study:

To review the fundamental principles of GPU computing.
To discuss performance optimization techniques for GPUs.
To survey current GPU applications in medical physics.

Main Methods:

Review of GPU architecture and parallel programming models.
Analysis of performance bottlenecks and optimization strategies.
Survey of literature on GPU applications in medical physics.

Main Results:

GPUs provide a competitive platform for massively parallel computing in medical physics.
Key application areas include image reconstruction, dose calculation, and treatment plan optimization.
Image processing tasks also benefit from GPU acceleration.

Conclusions:

GPU computing is a valuable tool for advancing medical physics research and clinical applications.
Understanding GPU principles and optimization is crucial for effective implementation.
The surveyed applications demonstrate the broad impact of GPUs in the field.