A novel reconstruction approach combining OSEM and split Bregman method for low dose CT. (September 2020)

• Record Type: Journal Article
• Authors: Sheng, Jinhua; Chen, Bin; Ma, Yangjie; Shi, Yuchen
• Is Part Of: Biomedical signal processing and control. Volume 62(2020)
• Publication Date: 2020-09
• Subjects: Low dose CT -- Image reconstruction -- OSEM-SBTV -- Split Bregman iteration -- Regularization; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102095
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel approach is proposed based on OSEM and split Bregman method (OSEM-SBTV) for low dose CT. Results show that OSEM-SBTV has better performance in suppressing noise and smoothing than the classical OSEM. The proposed approach can keep the reconstructed image smooth while maintaining the fine structure. Abstract: Purpose: Low dose CT imaging is an important research hotspot in the field of medical imaging. On the condition of low dose scanning, the commonly used filtered back projection (FBP) algorithm in the case of normal dose cannot meet the requirements with low signal-to-noise ratio (SNR), stripe artifacts and other problems. The algorithms of statistical iteration type can better handle low dose projection data. Existing regularization methods have been shown to deal with this problem to a large extent. Because their regular items are fixed, their adaptability to low dose conditions is not well. The main purpose of this paper is to explore the new method to improve the quality of CT reconstruction image at low dose condition. Methods: A novel approach is proposed based on OSEM and split Bregman method (OSEM-SBTV) for low dose CT. It includes two steps: OSEM solving image reconstruction and split Bregman method solving total variation denoising. Results: Compared with OSEM, results show that OSEM-SBTV has better performance in suppressing noise and smoothing than the classical OSEM. For comparison of profiles of Tikhonov, L1 and TV regularization models, the results of L1 norm are most affected by noise, and the profiles fluctuate greatly. The profile results of Tikhonov and TV norm are over smooth, which results in no representation of the profile information of the middle circle of the image at all in the middle of the profile. Conclusions: The proposed approach can keep the reconstructed image smooth while maintaining the fine structure. This is a good approach to deal with low dose CT image reconstruction.