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Medical Image Processing on the GPU: Past, Present and Future
Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA.
Department of Medical Imaging, University of Toronto, Toronto, Canada.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0003-0908-9470
Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA.
2013 (English)In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 17, no 8, 1073-1094 p.Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 17, no 8, 1073-1094 p.
Keyword [en]
Graphics processing unit (GPU), OpenGL, DirectX, CUDA, OpenCL, Filtering, Interpolation, Histogram estimation, Distance transforms, Image registration, Image segmentation, Image denoising, CT, PET, SPECT, MRI, fMRI, DTI, Ultrasound, Optical imaging, Microscopy
National Category
Medical Image Processing
URN: urn:nbn:se:liu:diva-93673DOI: 10.1016/ 000326662000015OAI: diva2:625940
Swedish Research Council, 2007-4786
Available from: 2013-06-05 Created: 2013-06-05 Last updated: 2014-11-17Bibliographically approved

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