Evaluation of FBP, ART and MLEM image reconstruction algorithms in sparse view CT studies

In clinical trials, Computed Tomography (CT) is widely used for diagnosis and treatment guidance. With the increasing use of CT in clinical practice, the issue of high radiation dose has become a significant concern. One way to reduce the dose in CT is by utilizing sparse view imaging. However, sparse view imaging often leads to artifacts in the reconstructed images due to the lack of data. This paper aims to examine and evaluate image reconstruction methods to introduce effective algorithms for sparse view studies. Common image reconstruction algorithms such as Maximum Likelihood Expectation Maximization (MLEM), Algebraic Reconstruction Technique (ART), and Filtered Back Projection (FBP) were reviewed. FBP and MLEM algorithms perform well when there is complete data, but due to the high speed of the FBP algorithm, it is best suited for such cases. However, when data is limited, FBP performs poorly, leading to a comparison between the ART and MLEM algorithms. The results indicate that MLEM performs better in sparse view studies. Quantitative parameters such as Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity Index (SSIM) were evaluated to assess the results. The findings suggest that FBP and MLEM algorithms perform better when data is complete, while MLEM algorithm excels in sparse view studies.