基于图形处理器加速的医学图像配准技术进展

针对目前医学图像配准技术无法满足临床实时性需求问题,对基于图形处理器(GPU)加速的医学图像配准技术进行综述探讨。首先对GPU通用计算进行概述,再以医学图像配准基本框架为主线,对近年来基于GPU加速的医学图像配准技术在国内外发展现状进行深入研究,并针对正电子发射型计算机断层显像(PET)和电子计算机断层扫描(CT)数据的非线性配准问题,分别基于中央处理器(CPU)和GPU平台进行配准实验,通过实验结果的对比,体现GPU加速配准技术的优越性。基于GPU加速的自由形变(FFD)和归一化互信息(NMI)结合的非线性配准方法配准后互信息值略低于CPU平台的配准结果,但其配准速度是CPU平台的12倍。基于GPU加速的配准算法在保持配准精度的基础上,配准速度都得到了很大的提升。

Development of medical image registration technology using GPU

The current medical image registration technology could not meet the real-time requirements for clinical diagnosis and treatment. Graphic Processing Unit (GPU) accelerated medical image registration technology was reviewed and discussed for this problem in this paper. The paper summarized GPU general purpose computation, studied current technology of medical image registration which based on GPU acceleration with the essential framework of medical image registration as main line, and implemented Positron Emission computed Tomography (PET) and Computed Tomography (CT) image registration experiments respectively on Central Processing Unit (CPU) and GPU computing platforms. The Normalized Mutual Information (NMI) value of GPU accelerated medical image registration based on Free Form Deformation ( FFD) and NMI was slightly smaller than that of CPU method, but the registration efficiency is 12 times than CPU method. Except keeping high registration accuracy, GPU accelerated medical image registration algorithms also get a lot of ascension in terms of registration speed.