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SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


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Parallel perfusion imaging processing using GPGPU

Author(s): F. Zhu, D. R. Gonzalez, T. Carpenter, M. Atkinson, J. Wardlaw

Abstract:
BACKGROUND AND PURPOSE: The objective of brain perfusion quantification is to generate parametric maps of relevant hemodynamic quantities such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) that can be used in diagnosis of acute stroke. These calculations involve deconvolution operations that can be very computationally expensive when using local Arterial Input Functions (AIF). As time is vitally important in the case of acute stroke, reducing the analysis time will reduce the number of brain cells damaged and increase the potential for recovery. METHODS: GPUs originated as graphics generation dedicated co-processors, but modern GPUs have evolved to become a more general processor capable of executing scientific computations. It provides a highly parallel computing environment due to its large number of computing cores and constitutes an affordable high performance computing method. In this paper, we will present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose Graphics Processor Units) using the CUDA programming model. We present the serial and parallel implementations of such algorithms and the evaluation of the performance gains using GPUs. RESULTS: Our method has gained a 5.56 and 3.75 speedup for CT and MR images respectively. CONCLUSIONS: It seems that using GPGPU is a desirable approach in perfusion imaging analysis, which does not harm the quality of cerebral hemodynamic maps but delivers results faster than the traditional computation.

Full version: Available here

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ISBN: 1872-7565 (Electronic) 0169-2607 (Linking)
Publication Year: 2012
Periodical: Comput Methods Programs Biomed
Periodical Number:
Volume:
Pages:
Author Address: Data-Intensive Research Group, School of Informatics, University of Edinburgh, Edinburgh, UK; SFC Brain Imaging Research Centre, Division of Clinical Neuroscience, University of Edinburgh, Edinburgh, UK.