Document Type : Research Paper

Authors

• Sayna Jamaati ¹
• Mohammad Ghorbanzadeh ¹
• Seyed Abolfazl Hosseini ²

¹ Faculty of Energy, SUT

² Director of Nuclear Engineering Department, Faculty of Energy, SUT

Abstract

In clinical trials, Computed Tomography (CT) is widely used to diagnose and guide treatment. As CT has become more prevalent in clinical practice, the issue of high radiation dose has gained much attention. It is possible to reduce the dose in CT by using sparse view imaging. The absence of data in this imaging mode, frequently results in artifacts in the reconstructed images. In this paper, attempted to examine and assess image reconstruction methods in order to introduce effective algorithms in sparse view studies. In order to achieve this, common image reconstruction algorithms: Maximum Likelihood Expectation Maximization (MLEM), Algebraic Reconstruction Technique (ART) and Filtered Back Projection (FBP), have been reviewed. FBP and MLEM algorithms showed good performance when the number of data is complete, but due to the very high speed of the FBP algorithm, it is recommended to use this algorithm in this cases. But when the number of data decreases, the FBP algorithm performs poorly, and the main comparison is between the ART algorithm and MLEM. The results show the better performance of MLEM in sparse view study. Also, in this paper, in order to evaluate the results, quantitative parameters like Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Structural Similarity index (SSIM) have been investigated and according to the results, when the data is complete, FBP and MLEM algorithms, and in sparse view studies, MLEM algorithm showed better performances.

Keywords

• Image Reconstruction
• Computed Tomography
• Sparse View
• MLEM
• ART
• FBP

Main Subjects

• RADIOLOGY AND NUCLEAR MEDICINE