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A Parallel Deconvolution Algorithm in Perfusion Imaging

Fan Zhu, David Rodriguez Gonzalez, Trevor Carpenter, Malcolm Atkinson, Joanna Wardlaw
Data-Intensive Research Group, School of Informatics, University of Edinburgh, Edinburgh, UK
First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB), 2011

@inproceedings{zhu2011parallel,

   title={A Parallel Deconvolution Algorithm in Perfusion Imaging},

   author={Zhu, F. and Gonzalez, D.R. and Carpenter, T. and Atkinson, M. and Wardlaw, J.},

   booktitle={Healthcare Informatics, Imaging and Systems Biology (HISB), 2011 First IEEE International Conference on},

   pages={278–283},

   year={2011},

   organization={IEEE}

}

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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. GPUs originated as graphics generation dedicated co-processors, but the 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 huge number of computing cores and constitutes an affordable high performance computing method. The objective of brain perfusion quantification is to generate parametric maps of relevant haemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that can be used in diagnosis of conditions such as stroke or brain tumors. These calculations involve deconvolution operations that in the case of using local Arterial Input Functions (AIF) can be very expensive computationally. We present the serial and parallel implementations of such algorithm and the evaluation of the performance gains using GPUs.
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