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A TV-minimization image-reconstruction algorithm without system matrix.

Zhiwei Qiao1, Yang Lu1

  • 1School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China.

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|July 26, 2021
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Summary
This summary is machine-generated.

A new rotation-based algorithm reconstructs CT images from sparse-view projections without a large system matrix. This method significantly saves memory and improves reconstruction speed while maintaining accuracy.

Keywords:
Rotation-based reconstructionTV minimizationcomputed tomographyiterative algorithmsystem matrix

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Imaging

Background:

  • Total Variation (TV) minimization is a key compressed sensing (CS) algorithm for accurate computed tomography (CT) image reconstruction from sparse-view projections.
  • Classical TV algorithms require a large system matrix, posing significant memory challenges for large-scale iterative reconstructions.

Purpose of the Study:

  • To develop and investigate a novel TV algorithm that eliminates the need for a system matrix, thereby addressing memory limitations.
  • To explore a TV algorithm based on image rotation for efficient sparse-view CT reconstruction.

Main Methods:

  • A rotation-based adaptive steepest descent-projection onto convex sets (R-ASD-POCS) algorithm was proposed and tested for parallel beam CT.
  • Simulation experiments utilized Shepp-Logan, FORBILD, and real CT image phantoms to assess inverse-crime capability, sparse reconstruction, and noise suppression.
  • The R-ASD-POCS algorithm was compared against the classical ASD-POCS algorithm.

Main Results:

  • The R-ASD-POCS algorithm demonstrated successful inverse-crime capability and accurate sparse reconstruction from noisy projections.
  • Similar image reconstruction accuracy to classical ASD-POCS was achieved, but with significantly reduced computational memory requirements.
  • The R-ASD-POCS algorithm exhibited faster reconstruction times compared to ASD-POCS.

Conclusions:

  • The proposed no-system-matrix R-ASD-POCS algorithm effectively overcomes the memory burden associated with large-scale iterative image reconstruction.
  • This novel approach offers a memory-efficient and faster alternative for sparse-view CT image reconstruction.
  • The scheme's integration with the ASD-POCS framework allows for potential extension to various iterative image reconstruction applications.