Q Zhu1,2, Y Wang1,2, M Zhu1,2, X Tao1,2, Z Bian1, J Ma1. 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. 2. Pazhou Lab, Guangzhou 510330, China.
Abstract
OBJECTIVE: To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. METHODS: A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. RESULTS: For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001). CONCLUSION: The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.
OBJECTIVE: To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. METHODS: A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. RESULTS: For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001). CONCLUSION: The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.
Entities:
Keywords:
CT metal artifacts; physical correction; projection interpolation
Authors: Sören Schüller; Stefan Sawall; Kai Stannigel; Markus Hülsbusch; Johannes Ulrici; Erich Hell; Marc Kachelrieß Journal: Med Phys Date: 2015-02 Impact factor: 4.071