A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images / 南方医科大学学报
Journal of Southern Medical University
; (12): 724-732, 2022.
Article
in Zh
| WPRIM
| ID: wpr-936369
Responsible library:
WPRO
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.
Key words
Full text:
1
Database:
WPRIM
Main subject:
Algorithms
/
Image Processing, Computer-Assisted
/
Water
/
Tomography, X-Ray Computed
/
Phantoms, Imaging
/
Signal-To-Noise Ratio
Type of study:
Prognostic_studies
Limits:
Humans
Language:
Zh
Journal:
Journal of Southern Medical University
Year:
2022
Document type:
Article