A Novel Image Reconstruction Approach on Thoracic SPECT Images for Instant and Accurate Response

Single-photon emission Computed Tomography (SPECT) is an emission imaging modality based on the administration of radiopharmaceuticals to patients. In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image often suffers from 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 employed as one of the strategies for correcting the resolution and improving the quality of the reconstructed SPECT images. This chapter presents a new 3D algorithm aimed at enhancing the quality of reconstructed thoracic SPECT images and reducing the noise level with the highest degree of accuracy. Two classes of the analytic algorithm are developed: simple back-projection (SBP) and filtered back-projection (FBP). The suggested algorithm comprises three steps. The first one involves denoising the acquired projections using the benefits of the complementary properties of both the Curvelet transform and the Wavelet transforms to achieve optimal noise reduction. The second step involves the simultaneous reconstruction of the axial slices using the 3D Ordered Subset Expectation Maximization (OSEM) algorithm. The last step involves post-processing 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 in the 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 closely approximates 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 the 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 for correcting the resolution of bone in SPECT images. Future research may focus on combining this algorithm with a deep learning method to obtain an optimal and innovative method of bone SPECT image reconstruction.