CBCT-to-CT synthesis with a hybrid of CycleGAN and latent diffusion

Clark DP, Badea CT (2019) Spectral data completion for dual-source X-ray CT. Med Imaging 2019: Phys Med Imaging Int Soc Opt Photonics 109481F. https://doi.org/10.1117/12.2512825

Deng L, Ji Y, Huang S, Yang X, Wang J (2023) Synthetic CT generation from CBCT using double-chain-CycleGAN. Comput Biol Med 161:106889. https://doi.org/10.1016/j.compbiomed.2021.106889

Article  CAS  PubMed  Google Scholar 

Posadzy M, Desimpel J, Vanhoenacker F (2018) Cone beam CT of the musculoskeletal system: clinical applications. Insights Imaging 9:35–45. https://doi.org/10.1007/s13244-017-0582-1

Article  PubMed  PubMed Central  Google Scholar 

White I, Hunt A, Bird T, Settatree S, Soliman H, Mcquaid D, Dearnaley D, Lalondrelle S, Bhide S (2021) Interobserver variability in target volume delineation for CT/MRI simulation and MRI-guided adaptive radiotherapy in rectal cancer. Brit J Radiol 94(1128):20210350. https://doi.org/10.1259/bjr.20210350

Article  PubMed  PubMed Central  Google Scholar 

Splinter M et al (2019) Dosimetric impact of interfractional variations for post-prostatectomy radiotherapy to the prostatic fossa-relevance for the frequency of position verification imaging and treatment adaptation. Front Oncol 9. https://doi.org/10.3389/fonc.2019.01191

Wang T, Liu X, Dai J et al (2023) An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image. Comput Biol Med 165:107377. https://doi.org/10.1016/j.compbiomed.2023.107377

Article  PubMed  Google Scholar 

Li et al (2023) Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT. Front Oncol 13. https://doi.org/10.3389/fonc.2023.1127866

Yang H, Sun J, Carass A et al (2020) Unsupervised MR-to-CT synthesis using structure-constrained CycleGAN. IEEE Trans Med Imaging 39(12):4249–4261. https://doi.org/10.1109/TMI.2020.3015379

Article  PubMed  Google Scholar 

Lu Q, Luo F, Shi J et al (2024) Synthetic CT generation from CBCT based on structural constraint cycle-EEM-GAN. Biomed Phys Eng Express 10(6):065016. https://doi.org/10.1088/2057-1976/ad7607

Article  Google Scholar 

Rombach R et al (2022) High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 10684-10695. https://doi.org/10.48550/arXiv.2112.10752

Wang Z, Yang Y, Chen Y, Yuan T et al (2024) Mutual information guided diffusion for zero-shot cross-modality medical image translation. IEEE Trans Med Imaging. https://ieeexplore.ieee.org/document/10485553

Li Y, Shao HC, Liang X, Chen L et al (2023) Zero-shot medical image translation via frequency-guided diffusion models. IEEE Trans Med Imaging. https://arxiv.org/abs/2304.02742

Peng J, Qiu RLJ, Wynne JF, Chang CW, Pan S et al (2024) CBCT-based synthetic CT image generation using conditional denoising diffusion probabilistic model. Med Phys. https://arxiv.org/abs/2303.02649

Fu L, Li X, Cai X et al (2024) Energy-guided diffusion model for CBCT-to-CT synthesis. Comput Med Imaging Graph 113:102344. https://doi.org/10.1016/j.compmedimag.2024.102344

Article  PubMed  Google Scholar 

Peng J, Gao Y, Chang CW et al (2024) Unsupervised bayesian generation of synthetic CT from CBCT using patient-specific score-based prior. Update in: Med Phys 2024 Dec 12. https://doi.org/10.1002/mp.17572

Li et al (2023) Zero-shot medical image translation via frequency-guided diffusion models. https://doi.org/10.48550/arXiv.2304.02742

Liu Xuan, Xie Yaoqin, Liu Chenbin et al (2024) Diffusion probabilistic priors for zero-shot low-dose CT image denoising. Med Phys 52:329–345. https://doi.org/10.1002/mp.17431

Article  PubMed  Google Scholar 

He K, Boley M, Dhariwal P, Schulman J, Levine S, Radford A, Winn J (2022) High-resolution image reconstruction with latent diffusion models from human brain activity. https://doi.org/10.1101/2022.11.18.517004

Kim J, Lee J, Kim J, Park S (2023) Adaptive latent diffusion model for 3d medical image to image translation: multi-modal magnetic resonance imaging study. Medical Imaging with Deep Learning (MIDL). https://doi.org/10.48550/arXiv.2311.00265

Zhao Y, Wang H, Yu C et al (2023) Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy. Med Phys 50(7):4399–4414. https://doi.org/10.1002/mp.16246

Article  PubMed  Google Scholar 

Jihong C, Kerun Q, Kaiqiang C et al (2023) CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma. Sci Rep 13(1):6624. https://doi.org/10.1038/s41598-023-33472-w

Wang L, Wang X, Vizziello, A Gamb P (2023) RSAAE: Residual Self-Attention-Based Autoencoder for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing 61:1–14, Art no. 5510614. https://doi.org/10.1109/TGRS.2023.3271719

Kanopoulos N, Vasanthavada N, Baker RL (1988) Design of an image edge detection filter using the Sobel operator. IEEE J Solid-State Circuits 23(2): 358-367. https://ieeexplore.ieee.org/document/996. Accessed 15 Jul 2024

Tancik M, Schwartz J, Beyer L, Thies J, Pfister H (2021) High-resolution image synthesis with latent diffusion models. Conf on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.48550/arXiv.2112.10752

Nichol AQ, Dhariwal P (2021) Improved denoising diffusion probabilistic models. In: Meila M, Zhang T (ed) Proceedings of the 38th international conference on machine learning, vol 139. PMLR, pp 8162-8171. https://doi.org/10.48550/arXiv.2102.09672. Accessed 18-24 Jul 2021

Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc IEEE Int Conf Comput Vis (ICCV), pp 2242-2251. https://ieeexplore.ieee.org/document/8237506

Kong L, Lian C, Huang D, Hu Y, Zhou Q (2021) Breaking the dilemma of medical image-to-image translation. Proc Adv Neural Inf Process Syst 34:1964–1978. https://doi.org/10.48550/arXiv.2110.06465

Article  Google Scholar 

Liu M-Y, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. Proc Adv Neural Inf Process Syst 30:1–9. https://doi.org/10.48550/arXiv.1703.00848

Article  CAS  Google Scholar 

Thummerer A, van der Bijl E, Galapon A Jr et al (2023) SynthRAD2023 Grand challenge dataset: generating synthetic CT for radiotherapy. Med Phys 50(7):4664–4674. https://doi.org/10.1002/mp.16529

Article  PubMed  Google Scholar 

Comments (0)

No login
gif