A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images.
10.12122/j.issn.1673-4254.2022.05.14
- Author:
Lei WANG
1
;
Yong Bo WANG
1
;
Zhao Ying BIAN
1
;
Jian Hua MA
1
;
Jing HUANG
1
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
deep learning;
material decomposition methods;
nonlocal spectral;
spectral CT
- MeSH:
Algorithms;
Humans;
Image Processing, Computer-Assisted/methods*;
Phantoms, Imaging;
Signal-To-Noise Ratio;
Tomography, X-Ray Computed/methods*;
Water
- From:
Journal of Southern Medical University
2022;42(5):724-732
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images.
METHODS:We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method.
RESULTS:The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the datamodel coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P < 0.001).
CONCLUSION:The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.
