A Framework for Guiding DDPM-Based Reconstruction of Damaged CT Projections Using Traditional Methods

Ge Wang, Hengyong Yu, and Bruno De Man. An outlook on x-ray ct research and development. Medical physics, 35(3):1051–1064, 2008.

Article  PubMed  Google Scholar 

Heang K Tuy. An inversion formula for cone-beam reconstruction. SIAM Journal on Applied Mathematics, 43(3):546–552, 1983.

Bruce D Smith. Image reconstruction from cone-beam projections: necessary and sufficient conditions and reconstruction methods. IEEE transactions on medical imaging, 4(1):14–25, 1985.

Emmanuel J Candes, Justin K Romberg, and Terence Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 59(8):1207–1223, 2006.

Emil Y Sidky and Xiaochuan Pan. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine & Biology, 53(17):4777, 2008.

Han-Ming Zhang, Lin-Yuan Wang, Bin Yan, Lei Li, Xiao-Qi Xi, and Li-Zhong Lu. Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography. Chinese Physics B, 22(7):078701, 2013.

Article  Google Scholar 

Ailong Cai, Linyuan Wang, Hanming Zhang, Bin Yan, Lei Li, Xiaoqi Xi, and Jianxin Li. Edge guided image reconstruction in linear scan ct by weighted alternating direction tv minimization. Journal of X-ray Science and Technology, 22(3):335–349, 2014.

PubMed  Google Scholar 

Moran Xu, Dianlin Hu, Fulin Luo, Fenglin Liu, Shaoyu Wang, and Weiwen Wu. Limited-angle x-ray ct reconstruction using image gradient \( \ell _0 \)-norm with dictionary learning. IEEE Transactions on Radiation and Plasma Medical Sciences, 5(1):78–87, 2020.

Google Scholar 

Yixing Huang, Oliver Taubmann, Xiaolin Huang, Viktor Haase, Guenter Lauritsch, and Andreas Maier. Scale-space anisotropic total variation for limited angle tomography. IEEE Transactions on Radiation and Plasma Medical Sciences, 2(4):307–314, 2018.

Article  Google Scholar 

Zhuoran Jiang, Yingxuan Chen, Yawei Zhang, Yun Ge, Fang-Fang Yin, and Lei Ren. Augmentation of cbct reconstructed from under-sampled projections using deep learning. IEEE transactions on medical imaging, 38(11):2705–2715, 2019.

Article  PubMed  PubMed Central  Google Scholar 

Minjae Lee, Hyemi Kim, and Hee-Joung Kim. Sparse-view ct reconstruction based on multi-level wavelet convolution neural network. Physica Medica, 80:352–362, 2020.

Article  PubMed  Google Scholar 

Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon. Solving inverse problems in medical imaging with score-based generative models. arXiv preprint arXiv:2111.08005, 2021.

Jian Fu, Jianbing Dong, and Feng Zhao. A deep learning reconstruction framework for differential phase-contrast computed tomography with incomplete data. IEEE Transactions on Image Processing, 29:2190–2202, 2019.

Google Scholar 

Ziheng Li, Wenkun Zhang, Linyuan Wang, Ailong Cai, Ningning Liang, Bin Yan, and Lei Li. A sinogram inpainting method based on generative adversarial network for limited-angle computed tomography. In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, volume 11072, pages 345–349. SPIE, 2019.

Juuso HJ Ketola, Helinä Heino, Mikael AK Juntunen, Miika T Nieminen, Samuli Siltanen, and Satu I Inkinen. Generative adversarial networks improve interior computed tomography angiography reconstruction. Biomedical physics & engineering express, 7(6):065041, 2021.

Changrong Shi, Yongshun Xiao, and Zhiqiang Chen. Dual-domain sparse-view ct reconstruction with transformers. Physica Medica, 101:1–7, 2022.

Article  PubMed  Google Scholar 

Hu Chen, Yi Zhang, Yunjin Chen, Junfeng Zhang, Weihua Zhang, Huaiqiang Sun, Yang Lv, Peixi Liao, Jiliu Zhou, and Ge Wang. Learn: Learned experts’ assessment-based reconstruction network for sparse-data ct. IEEE transactions on medical imaging, 37(6):1333–1347, 2018.

Article  PubMed  PubMed Central  Google Scholar 

Il Yong Chun, Zhengyu Huang, Hongki Lim, and Jeffrey A Fessler. Momentum-net: Fast and convergent iterative neural network for inverse problems. IEEE transactions on pattern analysis and machine intelligence, 45(4):4915–4931, 2020.

Ju Zhang, Weiwei Gong, Lieli Ye, Fanghong Wang, Zhibo Shangguan, and Yun Cheng. A review of deep learning methods for denoising of medical low-dose ct images. Computers in Biology and Medicine, page 108112, 2024.

Liutao Yang, Zhongnian Li, Rongjun Ge, Junyong Zhao, Haipeng Si, and Daoqiang Zhang. Low-dose ct denoising via sinogram inner-structure transformer. IEEE transactions on medical imaging, 42(4):910–921, 2022.

Article  Google Scholar 

Dayang Wang, Fenglei Fan, Zhan Wu, Rui Liu, Fei Wang, and Hengyong Yu. Ctformer: convolution-free token2token dilated vision transformer for low-dose ct denoising. Physics in Medicine & Biology, 68(6):065012, 2023.

Article  Google Scholar 

Feixiang Zhao, Mingzhe Liu, Zhihong Gao, Xin Jiang, Ruili Wang, and Lejun Zhang. Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose ct denoising. Computers in Biology and Medicine, 161:107029, 2023.

Article  PubMed  Google Scholar 

Qing Li, Runrui Li, Saize Li, Tao Wang, Yubin Cheng, Shuming Zhang, Wei Wu, Juanjuan Zhao, Yan Qiang, and Long Wang. Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network. Medical Physics, 51(2):1289–1312, 2024.

Article  PubMed  Google Scholar 

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256–2265. PMLR, 2015.

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.

Google Scholar 

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022.

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479–36494, 2022.

Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392–18402, 2023.

Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. Advances in Neural Information Processing Systems, 35:8633–8646, 2022.

Ruihan Yang, Prakhar Srivastava, and Stephan Mandt. Diffusion probabilistic modeling for video generation. Entropy, 25(10):1469, 2023.

Article  PubMed  PubMed Central  Google Scholar 

Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto. Diffusion-lm improves controllable text generation. Advances in Neural Information Processing Systems, 35:4328–4343, 2022.

Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong. Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933, 2022.

Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Fei Huang, and Songfang Huang. Seqdiffuseq: Text diffusion with encoder-decoder transformers. arXiv preprint arXiv:2212.10325, 2022.

Jiaming Liu, Rushil Anirudh, Jayaraman J Thiagarajan, Stewart He, K Aditya Mohan, Ulugbek S Kamilov, and Hyojin Kim. Dolce: A model-based probabilistic diffusion framework for limited-angle ct reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10498–10508, 2023.

Xuying Zhao, Wenjin Jiang, Xinting Zhang, Wenxiu Guo, Yunsong Zhao, and Xing Zhao. Image reconstruction based on nonlinear diffusion model for limited-angle computed tomography. Inverse Problems, 40(4):045015, 2024.

Article  Google Scholar 

Qi Gao and Hongming Shan. Cocodiff: a contextual conditional diffusion model for low-dose ct image denoising. In Developments in X-Ray Tomography XIV, volume 12242, pages 92–98. SPIE, 2022.

Qi Gao, Zilong Li, Junping Zhang, Yi Zhang, and Hongming Shan. Corediff: Contextual error-modulated generalized diffusion model for low-dose ct denoising and generalization. IEEE Transactions on Medical Imaging, 43(2):745–759, 2024.

Article  PubMed  Google Scholar 

Feiyang Liao, Yufei Tang, Qiang Du, Jiping Wang, Ming Li, and Jian Zheng. Domain progressive low-dose ct imaging using iterative partial diffusion model. IEEE Transactions on Medical Imaging, pages 1–1, 2024.

Ge Wang. A perspective on deep imaging. IEEE Access, 4:8914–8924, 2016.

Article  Google Scholar 

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234–241. Springer, 2015.

Cynthia H McCollough, Adam C Bartley, Rickey E Carter, Baiyu Chen, Tammy A Drees, Phillip Edwards, David R Holmes III, Alice E Huang, Farhana Khan, Shuai Leng, et al. Low-dose ct for the detection and classification of metastatic liver lesions: results of the 2016 low dose ct grand challenge. Medical physics, 44(10):e339–e352, 2017.

Ziheng Zhang, Minghan Yang, Huijuan Li, Shuai Chen, Jianye Wang, and Lei Xu. An innovative low-dose ct inpainting algorithm based on limited-angle imaging inpainting model. Journal of X-Ray Science and Technology, 31(1):131–152, 2023.

PubMed  Google Scholar 

Junhua Chen, Leonard Wee, Andre Dekker, and Inigo Bermejo. Improving reproducibility and performance of radiomics in low-dose ct using cycle gans. Journal of Applied Clinical Medical Physics, 23(10):e13739, 2022.

Ji He, Yongbo Wang, and Jianhua Ma. Radon inversion via deep learning. IEEE transactions on medical imaging, 39(6):2076–2087, 2020.

Article  PubMed  Google Scholar 

Comments (0)

No login
gif