Cuda Parallel Implementation of Image Reconstruction Algorithm for Positron Emission Tomography

Belzunce Ma, Verrastro Ca, E. Venialgo, Cohen Im
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引用次数: 12

Abstract

Although the use of iterative algorithms for image reconstruction in 3D Positron Emission Tomography (PET) has shown to produce images with better quality than analytical methods, they are computationally expensive. New Graphic Processor Units (GPUs) provide high performance at low cost and programming tools that make it possible to execute parallel algorithms in scientific applications. In this work, a GPU parallel implementation of the iterative reconstruction algorithm MLEM 3D has been developed using CUDA, a parallel model from NVIDIA. The Siddon algorithm was used as Projector and Backprojector. Acceleration factors up to 85 times were achieved, with respect to a single thread CPU implementation. The performance in GPU with Tesla and Fermi, which are respectively the first and the last generation of CUDA compatible architectures, has been compared. The image quality in each platform has been analyzed, showing a higher level of noise in GPU, due to race condition problems. The new features of Fermi architecture permitted to solve this problem using atomic operations.
Cuda并行实现正电子发射断层成像图像重建算法
虽然在三维正电子发射断层扫描(PET)中使用迭代算法进行图像重建已经显示出比分析方法产生更好质量的图像,但它们的计算成本很高。新的图形处理器单元(gpu)以低成本提供高性能和编程工具,使在科学应用中执行并行算法成为可能。在这项工作中,使用NVIDIA的并行模型CUDA开发了迭代重建算法MLEM 3D的GPU并行实现。投影和反向投影分别采用Siddon算法。相对于单线程CPU实现,实现了高达85倍的加速因子。比较了第一代和最后一代CUDA兼容架构Tesla和Fermi在GPU上的性能。对各平台的图像质量进行了分析,发现由于竞争条件问题,GPU的噪声水平较高。费米体系结构的新特性允许使用原子操作来解决这个问题。
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