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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-93673 |
| Date | January 2013 |
| Creators | Eklund, Anders, Dufort, Paul, Forsberg, Daniel, LaConte, Stephen |
| Publisher | Linköpings universitet, Medicinsk informatik, Linköpings universitet, Tekniska högskolan, Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV, Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA, Department of Medical Imaging, University of Toronto, Toronto, Canada, Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Article, review/survey, info:eu-repo/semantics/article, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Medical Image Analysis, 1361-8415, 2013 |
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