Open Access

ARTICLE

Fast and Accurate Thoracic SPECT Image Reconstruction

Afef Houimli^1,*, IssamBen Mhamed², Bechir Letaief^1,3,4, Dorra Ben-Sellem^1,3,4

1 University of Tunis El Manar, Higher Institute of Medical Technologies of Tunis, Research Laboratory in Biophysics and Medical Technologies LR13ES07, Tunis, 1006, Tunisia
2 University of Tunis, Higher National School of Engineers of Tunis, Signal Image and Energy Management Laboratory: LR13 ES03 SIME, ENSIT, Montfleury, 1008, Tunisia
3 Faculty of Medicine of Tunis, University of Tunis El Manar, Tunis, 1007, Tunisia
4 Nuclear Medicine Department, Salah AZAIEZ Institute, Tunis, 1006, Tunisia

* Corresponding Author: Afef Houimli. Email:

(This article belongs to the Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)

Computer Modeling in Engineering & Sciences 2022, 131(2), 881-904. https://doi.org/10.32604/cmes.2022.016705

Received 18 March 2021; Accepted 17 June 2021; Issue published 14 March 2022

Abstract

In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image has insufficient contrast, poor resolution and inaccurate volume of the tumor size due to physical degradation factors. Generally, nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images. This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy. The suggested algorithm is composed of three steps. The first one consists of denoising the acquired projections using the benefits of the complementary properties of both the Curvelet transform and the Wavelet transforms to provide the best noise reduction. The second step is a simultaneous reconstruction of the axial slices using the 3D Ordered Subset Expectation Maximization (OSEM) algorithm. The last step is post-processing of the reconstructed axial slices using one of the newest anisotropic diffusion models named Partial Differential Equation (PDE). The method is tested on two digital phantoms and clinical bone SPECT images. A comparative study with four algorithms reviewed on state of the art proves the significance of the proposed method. In simulated data, experimental results show that the plot profile of the proposed model keeps close to the original one compared to the other algorithms. Furthermore, it presents a notable gain in terms of contrast to noise ratio (CNR) and execution time. The proposed model shows better results in the computation of contrast metric with a value of 0.68 ± 7.2 and the highest signal to noise ratio (SNR) with a value of 78.56 ± 6.4 in real data. The experimental results prove that the proposed algorithm is more accurate and robust in reconstructing SPECT images than the other algorithms. It could be considered a valuable candidate to correct the resolution of bone in the SPECT images.

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Keywords

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