A Parallel Deconvolution Algorithm in Perfusion Imaging

Fan Zhu, D.R. Gonzalez, T. Carpenter, M. Atkinson, J. Wardlaw

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationHealthcare Informatics, Imaging and Systems Biology (HISB), 2011 First IEEE International Conference on
Pages278-283
Number of pages6
DOIs
Publication statusPublished - 1 Jul 2011

Keywords

  • blood vessels
  • brain
  • coprocessors
  • deconvolution
  • diseases
  • haemodynamics
  • haemorheology
  • medical image processing
  • parallel algorithms
  • patient diagnosis
  • tumours
  • CUDA programming model
  • GPGPU
  • arterial input functions
  • brain perfusion quantification
  • brain tumor
  • cerebral blood flow
  • cerebral blood volume
  • general purpose graphics processor units
  • graphics generation dedicated coprocessor
  • haemodynamic quantity
  • high performance computing
  • mean transit time
  • parallel computing
  • parallel deconvolution algorithm
  • parametric map
  • perfusion imaging
  • stroke
  • Arrays
  • Blood
  • Computed tomography
  • Deconvolution
  • Graphics processing unit
  • Matrix decomposition
  • Parallelization
  • Perfusion Imaging

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