A review on parallel medical image processing on GPU

Abstract

An efficient implementation are necessary, as most medical imaging methods are computational expensive, and the amount of medical imaging data is growing. Graphic processing units (GPUs) can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. This review investigates the use of GPUs to accelerate medical imaging methods. A set of criteria for efficient use of GPUs are defined. The review concludes that most medical image processing methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.