Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation

Abdelwahhab Boudjelal; Zoubeida Messali; Abderrahim Elmoataz; Bilal Attallah

doi:10.1016/j.jmir.2017.09.005

Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation

J Med Imaging Radiat Sci. 2017 Dec;48(4):385-393. doi: 10.1016/j.jmir.2017.09.005. Epub 2017 Nov 1.

Authors

Abdelwahhab Boudjelal¹, Zoubeida Messali², Abderrahim Elmoataz³, Bilal Attallah⁴

Affiliations

¹ Electronics Department, University of Mohammed Boudiaf-M'sila, M'sila, Algeria; Image Team, GREYC Laboratory, University of Caen Normandy, Caen Cedex, France. Electronic address: abdelwahhab.boudjelal@unicaen.fr.
² Electronics Department, University of Mohamed El Bachir El Ibrahimi-Bordj Bou Arréridj, Bordj Bou Arréridj, Algeria.
³ Image Team, GREYC Laboratory, University of Caen Normandy, Caen Cedex, France.
⁴ Electronics Department, University of Mohammed Boudiaf-M'sila, M'sila, Algeria; Image Team, GREYC Laboratory, University of Caen Normandy, Caen Cedex, France.

PMID: 31047474
DOI: 10.1016/j.jmir.2017.09.005

Abstract

Context: There has been considerable progress in the instrumentation for data measurement and computer methods for generating images of measured PET data. These computer methods have been developed to solve the inverse problem, also known as the "image reconstruction from projections" problem.

Aim: In this paper, we propose a modified Simultaneous Algebraic Reconstruction Technique (SART) algorithm to improve the quality of image reconstruction by incorporating total variation (TV) minimization into the iterative SART algorithm.

Methodology: The SART updates the estimated image by forward projecting the initial image onto the sinogram space. Then, the difference between the estimated sinogram and the given sinogram is back-projected onto the image domain. This difference is then subtracted from the initial image to obtain a corrected image. Fast total variation (FTV) minimization is applied to the image obtained in the SART step. The second step is the result obtained from the previous FTV update. The SART and the FTV minimization steps run iteratively in an alternating manner. Fifty iterations were applied to the SART algorithm used in each of the regularization-based methods. In addition to the conventional SART algorithm, spatial smoothing was used to enhance the quality of the image. All images were sized at 128 × 128 pixels.

Results: The proposed algorithm successfully accomplished edge preservation. A detailed scrutiny revealed that the reconstruction algorithms differed; for example, the SART and the proposed FTV-SART algorithm effectively preserved the hot lesion edges, whereas artifacts and deviations were more likely to occur in the ART algorithm than in the other algorithms.

Conclusions: Compared to the standard SART, the proposed algorithm is more robust in removing background noise while preserving edges to suppress the existent image artifacts. The quality measurements and visual inspections show a significant improvement in image quality compared to the conventional SART and Algebraic Reconstruction Technique (ART) algorithms.

Keywords: PET; SART algorithm; fast total variation; image reconstruction; regularization.

MeSH terms

Algorithms*
Image Processing, Computer-Assisted* / standards
Mathematical Concepts
Positron-Emission Tomography / methods*
Quality Improvement
Time Factors