Fast and Accurate Thoracic SPECT Image Reconstruction

Afef Houimli,Bechir Letaief,Issamben Mhamed,Dorra Ben-Sellem

doi:10.32604/cmes.2022.016705

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.

Highlights

Single-Photon Emission Computed Tomography (SPECT) is an emission imaging modality based on the administration of radiopharmaceuticals to patients
Line (A) presents the noisy projection images, Line (B) depicts the results of the projection image denoised by the Wavelet transform, Line (C) illustrates the results of the residual images, Line (D) illustrates the results of the residual image denoised by the Curvelet transform, Column (A) presents the reconstructed axial slice, Column (B) the enhanced axial slice by EDP, Column (C) the coronal enhanced slice and Column (D) the sagittal enhanced slice
The tomographic Shepp-Logan image was reconstructed respectively with Maximum Likelihood Expectation Maximization (MLEM) (120 iterations), 2D-Ordered Subset Expectation Maximization (OSEM) (1024 iterations and 4 subsets) with a three-dimensional post filtering with a Metz filter, CNNR

Summary

Introduction

Single-Photon Emission Computed Tomography (SPECT) is an emission imaging modality based on the administration of radiopharmaceuticals to patients. Due to the radioactivity disintegration, to the acquisition and the reconstruction algorithms procedure, the reconstructed SPECT image suffers from poor spatial resolution, bad contrast and an important noise level which makes it difficult to detect the bone lesions and allows an inaccurate diagnosis. The analytic reconstruction, such as Filtered Back-Projection (FBP) [4], generated significant artifacts and induced more noise because of the limited number of acquiring projections [5]. The Curvelet transform is based on the anisotropic graduation principle, which is quite different from isotropic wavelet scaling These characteristics are beneficial for the development of denoising SPECT image algorithms. Low density characterizes very high signals that have line, curve and hyperlink singularities [24]

Methods

Results

Conclusion