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Article

Generation of Vertebra Micro-CT-like Image from MDCT: A Deep-Learning-Based Image Enhancement Approach

1
Department of Radiology, Peking University Third Hospital, Beijing 100191, China
2
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jasper Nijkamp
Tomography 2021, 7(4), 767-782; https://doi.org/10.3390/tomography7040064
Received: 2 September 2021 / Revised: 5 November 2021 / Accepted: 9 November 2021 / Published: 12 November 2021
This paper proposes a deep-learning-based image enhancement approach that can generate high-resolution micro-CT-like images from multidetector computed tomography (MDCT). A total of 12,500 MDCT and micro-CT image pairs were obtained from 25 vertebral specimens. Then, a pix2pixHD model was trained and evaluated using the structural similarity index measure (SSIM) and Fréchet inception distance (FID). We performed subjective assessments of the micro-CT-like images based on five aspects. Micro-CT and micro-CT-like image-derived trabecular bone microstructures were compared, and the underlying correlations were analyzed. The results showed that the pix2pixHD method (SSIM, 0.804 ± 0.037 and FID, 43.598 ± 9.108) outperformed the two control methods (pix2pix and CRN) in enhancing MDCT images (p < 0.05). According to the subjective assessment, the pix2pixHD-derived micro-CT-like images showed no significant difference from the micro-CT images in terms of contrast and shadow (p > 0.05) but demonstrated slightly lower noise, sharpness and trabecular bone texture (p < 0.05). Compared with the trabecular microstructure parameters of micro-CT images, those of pix2pixHD-derived micro-CT-like images showed no significant differences in bone volume fraction (BV/TV) (p > 0.05) and significant correlations in trabecular thickness (Tb.Th) and trabecular spacing (Tb.Sp) (Tb.Th, R = 0.90, p < 0.05; Tb.Sp, R = 0.88, p < 0.05). The proposed method can enhance the resolution of MDCT and obtain micro-CT-like images, which may provide new diagnostic criteria and a predictive basis for osteoporosis and related fractures. View Full-Text
Keywords: computed tomography; osteoporosis; vertebra; trabecular bone; deep learning; structure analysis computed tomography; osteoporosis; vertebra; trabecular bone; deep learning; structure analysis
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MDPI and ACS Style

Jin, D.; Zheng, H.; Zhao, Q.; Wang, C.; Zhang, M.; Yuan, H. Generation of Vertebra Micro-CT-like Image from MDCT: A Deep-Learning-Based Image Enhancement Approach. Tomography 2021, 7, 767-782. https://doi.org/10.3390/tomography7040064

AMA Style

Jin D, Zheng H, Zhao Q, Wang C, Zhang M, Yuan H. Generation of Vertebra Micro-CT-like Image from MDCT: A Deep-Learning-Based Image Enhancement Approach. Tomography. 2021; 7(4):767-782. https://doi.org/10.3390/tomography7040064

Chicago/Turabian Style

Jin, Dan, Han Zheng, Qingqing Zhao, Chunjie Wang, Mengze Zhang, and Huishu Yuan. 2021. "Generation of Vertebra Micro-CT-like Image from MDCT: A Deep-Learning-Based Image Enhancement Approach" Tomography 7, no. 4: 767-782. https://doi.org/10.3390/tomography7040064

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