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GPU-based Branchless Distance-Driven Projection and Backprojection.

Rui Liu1, Lin Fu2, Bruno De Man2

  • 1Wake Forest University Health Sciences, Winston-Salem, NC 27103 USA.

IEEE Transactions on Computational Imaging
|January 16, 2018
PubMed
Summary
This summary is machine-generated.

A new graphics processing unit (GPU) algorithm makes distance-driven (DD) projection and backprojection for computed tomography (CT) faster. This branchless DD method significantly accelerates 3D cone beam CT image reconstruction.

Keywords:
GPUbackprojectionbranchless distance-drivencomputed tomographyprojectionreconstruction

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Area of Science:

  • Medical physics
  • Computer science
  • Image processing

Background:

  • Distance-driven (DD) projection and backprojection are crucial for CT image reconstruction.
  • Typical DD implementations are inefficient on parallel devices like GPUs due to irregular branching.
  • Factorizing DD into branchless steps enables massive vectorization.

Purpose of the Study:

  • To implement and evaluate a highly parallel, branchless DD algorithm for 3D cone beam CT on GPUs.
  • To leverage GPU texture memory and hardware interpolation for computational speed.

Main Methods:

  • Developed a branchless distance-driven algorithm by factorizing the operation into integration, linear interpolation, and differentiation.
  • Utilized GPU texture memory and hardware interpolation for efficient computation.
  • Integrated the algorithm into iterative reconstruction for 3D cone beam CT.

Main Results:

  • Achieved 137x speedup for forward projection and 188x for backprojection compared to a single-thread CPU.
  • Outperformed a 32-thread CPU implementation with 8x and 10x speedups, respectively.
  • Generated visually identical images to CPU-based methods using simulation and real datasets.

Conclusions:

  • The proposed GPU-based branchless DD algorithm offers significant acceleration for 3D cone beam CT.
  • This method maintains image quality comparable to traditional CPU implementations.
  • The branchless approach effectively addresses GPU inefficiency caused by irregular branching in DD operations.