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Parallel perfusion imaging processing using GPGPU.

Fan Zhu1, David Rodriguez Gonzalez, Trevor Carpenter

  • 1Data-Intensive Research Group, School of Informatics, University of Edinburgh, Edinburgh, UK. F.Zhu@ed.ac.uk

Computer Methods and Programs in Biomedicine
|July 25, 2012
PubMed
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Accelerating brain perfusion quantification using Graphics Processing Units (GPUs) significantly reduces analysis time for acute stroke diagnosis. This GPGPU approach enhances computational efficiency without compromising the quality of hemodynamic maps.

Area of Science:

  • Medical imaging
  • Computational neuroscience
  • High-performance computing

Background:

  • Brain perfusion quantification generates hemodynamic maps (CBF, CBV, MTT) crucial for acute stroke diagnosis.
  • Deconvolution operations for these maps are computationally intensive, especially with local Arterial Input Functions (AIF).
  • Time is critical in acute stroke, making faster analysis essential to minimize brain damage and improve recovery.

Purpose of the Study:

  • To implement and evaluate a deconvolution algorithm for brain perfusion quantification on General Purpose Graphics Processor Units (GPGPU).
  • To assess the performance gains achieved by utilizing GPGPU acceleration for medical image analysis.
  • To demonstrate the feasibility of GPGPU in reducing analysis time for critical diagnoses.

Main Methods:

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  • Leveraged the CUDA programming model for implementing deconvolution algorithms on GPGPU.
  • Developed both serial and parallel implementations of the deconvolution algorithms.
  • Evaluated the performance improvements offered by GPU acceleration compared to traditional methods.

Main Results:

  • Achieved a 5.56x speedup for CT image analysis.
  • Observed a 3.75x speedup for MR image analysis.
  • Demonstrated significant performance gains through parallel processing on GPUs.

Conclusions:

  • GPGPU is a valuable approach for accelerating perfusion imaging analysis.
  • The use of GPGPU does not negatively impact the quality of cerebral hemodynamic maps.
  • Faster results from GPGPU enable more timely diagnoses and interventions in acute stroke cases.