Deep learning algorithm for lung CT synthesis based on iterative registration and perceptual loss(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第1期
- Page:
- 59-66
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Deep learning algorithm for lung CT synthesis based on iterative registration and perceptual loss
- Author(s):
- YANG Tao1; 2; HUANG Miao1; LIU Cong3; HU Zhihua1; TAO Lili1; ZHANG Shuping1
- 1. School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China 2. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China 3. School of Internet of Things Engineering, Shanghai Business School, Shanghai 201400, China
- Keywords:
- Keywords: cone beam computed tomography CycleGAN perceptual loss Elastix image synthesis
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2025.01.009
- Abstract:
- Abstract: Objective To synthesize high-quality synthetic CT (sCT) images from cone beam CT (CBCT) by learning lung CT domain image features with a deep learning algorithm. Methods A sCT generation algorithm which employs perceptual loss-based cyclic generative adversarial network model (CycleGAN) and iterative registration was presented. CycleGAN model was trained to generate high-quality sCT images by combining perceptual loss and cycle consistency loss and Elastix was used to register the generated sCT image and the planned CT (pCT) image, and iterate CycleGAN generator model. Results Experiments were conducted on the obtained pCT and CBCT data of 70 patients with lung tumors. From a quantitative perspective, the SSIM between sCT generated by the proposed algorithm and pCT was improved by 11.9% as compared with that between CBCT and pCT, increasing from 0.825 to 0.923 additionally, RMSE dropped from 110.97 HU to 78.62 HU, PSNR increased from 32.21 dB to 34.74 dB, and mutual information increased from 1.187 to 1.418. The visual evaluation revealed that the proposed algorithm greatly eliminated the scattering artifacts of CBCT slices, highlighted the bone structure, and repaired the soft tissue structure. The comparisons with U-CycleGAN, R-CycleGAN and CUT models confirmed the effectiveness of the proposed algorithm. Conclusion Using the proposed algorithm for sCT images generation can effectively reduce the dose error and structural error between CBCT and pCT, making it possible to apply the proposed algorithm to accurate dose calculations and assist doctors in clinical diagnosis.
Last Update: 2025-01-19