Medical image processing on the GPU - Past, present and future

被引:280
|
作者
Eklund, Anders
Dufort, Paul [1 ]
Forsberg, Daniel [2 ,3 ,4 ]
LaConte, Stephen M. [5 ]
机构
[1] Univ Toronto, Dept Med Imaging, Toronto, ON, Canada
[2] Linkoping Univ, Dept Biomed Engn, Div Med Informat, Linkoping, Sweden
[3] Linkoping Univ, Ctr Med Image Sci & Visualizat CMIV, Linkoping, Sweden
[4] Sectra, Linkoping, Sweden
[5] Virginia Tech Wake Forest Univ, Sch Biomed Engn & Sci, Blacksburg, VA USA
基金
瑞典研究理事会;
关键词
Medical imaging; Image processing; Image reconstruction; Graphics processing unit (GPU); CUDA; BEAM CT RECONSTRUCTION; OPTICAL-COHERENCE TOMOGRAPHY; REAL-TIME RECONSTRUCTION; FAST FOURIER-TRANSFORM; ITERATIVE RECONSTRUCTION; PET RECONSTRUCTION; MUTUAL-INFORMATION; FUNCTIONAL CONNECTIVITY; ANISOTROPIC DIFFUSION; TECHNIQUE SART;
D O I
10.1016/j.media.2013.05.008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges. (C) 2013 Elsevier B.V. All rights reserved.
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页码:1073 / 1094
页数:22
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