Low-dose CT Reconstruction Based on Deep Energy Models

ZHU Yuanzheng; LV Qiwen; GUAN Yu; LIU Qiegen

doi:10.15953/j.ctta.2021.077

Volume 31 Issue 6

Nov. 2022

Turn off MathJax

Article Contents

Abstract

References

Supplements

CT Theory and Applications > 2022 > 31(6): 709-720. > DOI: 10.15953/j.ctta.2021.077

ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction based on deep energy models[J]. CT Theory and Applications, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077. (in Chinese).

Citation:

ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction based on deep energy models[J]. CT Theory and Applications, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077. (in Chinese).

Citation:

ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction based on deep energy models[J]. CT Theory and Applications, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077. (in Chinese).

PDF (3195 KB)

Low-dose CT Reconstruction Based on Deep Energy Models

School of Information Engineering, Nanchang University, Nanchang 330036, China

More Information

Received Date: December 22, 2021
Revised Date: February 13, 2022
Accepted Date: February 15, 2022
Available Online: April 29, 2022
Published Date: November 02, 2022

Graphical Abstract

Abstract

Abstract

Reducing the dose of computed tomography (CT) is essential for reducing the radiation risk in clinical applications. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Unlike most existing prior-driven algorithms that benefit from manually designed prior functions or supervised learning schemes, in this paper, we use an energy-based deep model to learn the prior knowledge of normal-dose CT, and then in the iterative reconstruction phase, we integrate data consistency as a conditional item into the iterative generation model of low-dose CT, and realize the low-dose CT reconstruction through the prior experience of iterative updating training of Langevin dynamics. The experimental results show that the proposed method hold excellent noise reduction and detail retention capabilities.
- low-dose CT,
- deep learning,
- Langevin dynamics,
- deep energy based model

FullText(HTML)

References (34)

References

[1]	BRENNER D J, HALL E J. Computed tomography: An increasing source of radiation exposure[J]. New England Journal of Medicine, 2007, 357(22): 2277−2284. doi: 10.1056/NEJMra072149
[2]	KALRA M K, MAHER M M, TOTH T L, et al. Strategies for CT radiation dose optimization[J]. Radiology, 2004, 230(3): 619−628. doi: 10.1148/radiol.2303021726
[3]	韩泽芳, 上官宏, 张雄, 等. 基于深度学习的低剂量CT成像算法研究进展[J]. CT理论与应用研究, 2022, 31(1): 118-136. DOI: 10.15953/j.1004-4140.2022.31.01.14. HAN Z F, SHANGGUAN H, ZHANG X, et al. Research progress of low-dose CT imaging algorithm based on deep learning[J]. CT Theory and Applications, 2022, 31(1): 118-136. DOI: 10.15953/j.1004-4140.2022.31.01.14. (in Chinese).
[4]	TIAN Z, JIA X, YUAN K, et al. Low-dose CT reconstruction via edge-preserving total variation regularization[J]. Physics in Medicine & Biology, 2011, 56(18): 5949.
[5]	MA J, LIANG Z, FAN Y, et al. A study on CT sinogram statistical distribution by information divergence theory[C]//2011 IEEE Nuclear Science Symposium Conference Record. IEEE, 2011: 3191-3196.
[6]	ELBAKRI I A, FESSLER J A. Statistical image reconstruction for polyenergetic X-ray computed tomography[J]. IEEE Transactions on Medical Imaging, 2002, 21(2): 89−99. doi: 10.1109/42.993128
[7]	ZHANG K, ZUO W, CHEN Y, et al. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142−3155. doi: 10.1109/TIP.2017.2662206
[8]	LEFKIMMIATIS S. Universal denoising networks: A novel CNN architecture for image denoising[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3204-3213.
[9]	ZHANG K, ZUO W, ZHANG L. FFDNet: Toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4608−4622. doi: 10.1109/TIP.2018.2839891
[10]	CHEN H, ZHANG Y, ZHANG W, et al. Low-dose CT via convolutional neural network[J]. Biomedical Optics Express, 2017, 8(2): 679−694. doi: 10.1364/BOE.8.000679
[11]	CHEN H, ZHANG Y, KALRA M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2524−2535. doi: 10.1109/TMI.2017.2715284
[12]	WÜRFL T, GHESU F C, CHRISTLEIN V, et al. Deep learning computed tomography[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2016: 432-440.
[13]	CHENG L, AHN S, ROSS S G, et al. Accelerated iterative image reconstruction using a deep learning based leapfrogging strategy[C]//International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017: 715-720.
[14]	HAN Y S, YOO J, YE J C. Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis[J]. arXiv preprint arXiv: 1611.06391, 2016.
[15]	JIN K H, MCCANN M T, FROUSTEY E, et al. Deep convolutional neural network for inverse problems in imaging[J]. IEEE Transactions on Image Processing, 2017, 26(9): 4509−4522. doi: 10.1109/TIP.2017.2713099
[16]	RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2015: 234-241.
[17]	HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[18]	BAGUER D O, LEUSCHNER J, SCHMIDT M. Computed tomography reconstruction using deep image prior and learned reconstruction methods[J]. Inverse Problems, 2020, 36(9): 094004. doi: 10.1088/1361-6420/aba415
[19]	KINGMA D P, WELLING M. Auto-encoding variational bayes[J]. arXiv preprint arXiv: 1312.6114, 2013.
[20]	DINH L, KRUEGER D, BENGIO Y. Nice: Non-linear independent components estimation[J]. arXiv preprint arXiv: 1410.8516, 2014.
[21]	GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[J]. Advances in Neural Information Processing Systems, 2014: 27.
[22]	NOWOZIN S, CSEKE B, TOMIOKA R. F-GAN: Training generative neural samplers using variational divergence minimization[C]//Proceedings of the 30 th International Conference on Neural Information Processing Systems, 2016: 271-279.
[23]	ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks[C]// International Conference on Machine Learning. PMLR, 2017: 214-223.
[24]	蔡宁, 王世杰, 陈璐杰, 等. 基于渐进式网络处理的低剂量Micro-CT成像方法[J]. CT理论与应用研究, 2020,29(4): 435−446. DOI: 10.15953/j.1004-4140.2020.29.04.06. CAI N, WANG S J, CHEN L J. Low-dose Micro-CT imaging method based on progressive network processing[J]. CT Theory and Applications, 2020, 29(4): 435−446. DOI: 10.15953/j.1004-4140.2020.29.04.06. (in Chinese).
[25]	NIU C, LI M Z, FAN F L, et al. Suppression of correlated noises with similarity-based unsupervised deep learning[J]. arXiv: 2011.03384.
[26]	WU D, KIM K, E L FAKHRI G, et al. Iterative low-dose CT reconstruction with priors trained by artificial neural network[J]. IEEE Transactions on Medical Imaging, 2017, 36(12): 2479−2486. doi: 10.1109/TMI.2017.2753138
[27]	ZHANG F Q, ZHANG M H, QIN B J, et al. REDAEP: Robust and enhanced denoising autoencoding prior for sparse-view CT reconstruction[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 2021, 5(1): 108−119. doi: 10.1109/TRPMS.2020.2989634
[28]	LECUN Y, CHOPRA S, HADSELL R, et al. A tutorial on energy-based learning[M]. Predicting Structured Data, 2006: 191-241.
[29]	DU Y, MORDATCH I. Implicit generation and modeling with energy based models[J]. Advances in Neural Information Processing Systems, 2019: 32.
[30]	NIJKAMP E, HILL M, ZHU S C, et al. Learning non-convergent non-persistent short-run MCMC toward energy-based model[J]. arXiv preprint arXiv: 1904.09770, 2019.
[31]	WELLING M, TEH Y W, Bayesian learning via stochastic gradient Langevin dynamics[C]// International Conference on International Conference on Machine Learning. Omnipress, 2011.
[32]	ERDOGAN H, FESSLER J A. Monotonic algorithms for transmission tomography[C]//5th IEEE EMBS International Summer School on Biomedical Imaging, 2002.
[33]	DEVOLDER O, GLINEUR F, NESTEROV Y. First-order methods of smooth convex optimization with inexact oracle[J]. Mathematical Programming, 2014, 146(1): 37−75.
[34]	YIN X, ZHAO Q, LIU J, et al. Domain progressive 3D residual convolution network to improve low-dose CT imaging[J]. IEEE transactions on medical imaging, 2019, 38(12): 2903−2913. doi: 10.1109/TMI.2019.2917258