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Fast parallel implementation for total variation constrained algebraic reconstruction technique.

Shunli Zhang1, Yu Qiang1

  • 1School of Information Science and Technology, Northwest University, Xi'an, China.

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|May 9, 2022
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Summary
This summary is machine-generated.

This study introduces a faster algorithm for computed tomography (CT) image reconstruction using algebraic reconstruction technique with total variation (ART-TV). The new method significantly speeds up the process for high-resolution images, making CT scans more efficient.

Keywords:
Computed tomography (CT)algebraic reconstruction techniqueimage reconstructionparallel computingtotal variation

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Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Computed tomography (CT) image reconstruction quality is often limited by sparse and noisy projection data.
  • The total variation (TV) constrained algebraic reconstruction technique (ART) improves quality but is computationally intensive, especially for high-resolution images.

Purpose of the Study:

  • To develop a computationally efficient algorithm for ART-TV image reconstruction.
  • To accelerate the iterative process of ART-TV for high-resolution CT imaging.

Main Methods:

  • Proposed a fast algorithm for system matrix calculation in the line intersection model.
  • Implemented parallel computing using multithreading and graphics processing units (GPUs) to accelerate ART iteration and TV minimization.
  • Utilized Siddon algorithm for comparison in conventional single-threaded CPU implementation.

Main Results:

  • The parallel implementation significantly accelerates ART-TV algorithm iterations.
  • Achieved reconstruction of a 2048 × 2048 image in approximately 2.2 seconds per iteration on a ten-core platform.
  • Demonstrated a 23-fold speedup compared to conventional single-threaded CPU implementations.

Conclusions:

  • The proposed parallel implementation approach for ART-TV is highly efficient and accurate.
  • This acceleration is crucial for reconstructing high-resolution CT images from sparse and noisy data.
  • The method offers a substantial improvement in CT image reconstruction speed and practicality.