Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

Overview

Journal J Digit Imaging

Publisher Springer

Specialties Medical Informatics
Radiology

Date 2018 Feb 22

PMID 29464432

Citations 56

Authors

Xin Yi

Paul Babyn

Affiliations

Soon will be listed here.

Abstract

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

Citing Articles

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CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data.

Montgomery M, Andersen F, Mathiasen R, Borgwardt L, Andersen K, Ladefoged C Diagnostics (Basel). 2025; 14(24.

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Improvement in Image Quality of Low-Dose CT of Canines with Generative Adversarial Network of Anti-Aliasing Generator and Multi-Scale Discriminator.

Son Y, Jeong S, Hong Y, Lee J, Jeon B, Choi H Bioengineering (Basel). 2024; 11(9).

PMID: 39329686 PMC: 11428420. DOI: 10.3390/bioengineering11090944.

Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.

Zhao F, Liu M, Xiang M, Li D, Jiang X, Jin X J Imaging Inform Med. 2024; .

PMID: 39231886 DOI: 10.1007/s10278-024-01213-8.

References

La Riviere P . Penalized-likelihood sinogram smoothing for low-dose CT. Med Phys. 2005; 32(6):1676-83. DOI: 10.1118/1.1915015. View

Chen H, Zhang Y, Kalra M, Lin F, Chen Y, Liao P . Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network. IEEE Trans Med Imaging. 2017; 36(12):2524-2535. PMC: 5727581. DOI: 10.1109/TMI.2017.2715284. View

Ramani S, Fessler J . A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction. IEEE Trans Med Imaging. 2011; 31(3):677-88. PMC: 3298196. DOI: 10.1109/TMI.2011.2175233. View

Mao X, Li Q, Xie H, Lau R, Wang Z, Smolley S . On the Effectiveness of Least Squares Generative Adversarial Networks. IEEE Trans Pattern Anal Mach Intell. 2018; 41(12):2947-2960. DOI: 10.1109/TPAMI.2018.2872043. View

Yi X, Eramian M . LBP-Based Segmentation of Defocus Blur. IEEE Trans Image Process. 2016; 25(4):1626-38. DOI: 10.1109/TIP.2016.2528042. View

Beister M, Kolditz D, Kalender W . Iterative reconstruction methods in X-ray CT. Phys Med. 2012; 28(2):94-108. DOI: 10.1016/j.ejmp.2012.01.003. View

Bouman C, Sauer K . A generalized Gaussian image model for edge-preserving MAP estimation. IEEE Trans Image Process. 1993; 2(3):296-310. DOI: 10.1109/83.236536. View

Kang E, Min J, Ye J . A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys. 2017; 44(10):e360-e375. DOI: 10.1002/mp.12344. View

Xu Q, Yu H, Mou X, Zhang L, Hsieh J, Wang G . Low-dose X-ray CT reconstruction via dictionary learning. IEEE Trans Med Imaging. 2012; 31(9):1682-97. PMC: 3777547. DOI: 10.1109/TMI.2012.2195669. View

10.

Wang Z, Bovik A, Sheikh H, Simoncelli E . Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004; 13(4):600-12. DOI: 10.1109/tip.2003.819861. View

11.

Zhu X, Cohen S, Schiller S, Milanfar P . Estimating spatially varying defocus blur from a single image. IEEE Trans Image Process. 2013; 22(12):4879-91. DOI: 10.1109/TIP.2013.2279316. View

12.

Chen Y, Gao D, Nie C, Luo L, Chen W, Yin X . Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior. Comput Med Imaging Graph. 2009; 33(7):495-500. DOI: 10.1016/j.compmedimag.2008.12.007. View

13.

Manduca A, Yu L, Trzasko J, Khaylova N, Kofler J, McCollough C . Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys. 2009; 36(11):4911-9. PMC: 4108640. DOI: 10.1118/1.3232004. View

14.

Wolterink J, Leiner T, Viergever M, Isgum I . Generative Adversarial Networks for Noise Reduction in Low-Dose CT. IEEE Trans Med Imaging. 2017; 36(12):2536-2545. DOI: 10.1109/TMI.2017.2708987. View

15.

Ha S, Mueller K . Low dose CT image restoration using a database of image patches. Phys Med Biol. 2015; 60(2):869-82. DOI: 10.1088/0031-9155/60/2/869. View

16.

Lubner M, Pickhardt P, Tang J, Chen G . Reduced image noise at low-dose multidetector CT of the abdomen with prior image constrained compressed sensing algorithm. Radiology. 2011; 260(1):248-56. DOI: 10.1148/radiol.11101380. View

17.

Po D, Do M . Directional multiscale modeling of images using the contourlet transform. IEEE Trans Image Process. 2006; 15(6):1610-20. DOI: 10.1109/tip.2006.873450. View

18.

Balda M, Hornegger J, Heismann B . Ray contribution masks for structure adaptive sinogram filtering. IEEE Trans Med Imaging. 2012; 31(6):1228-39. DOI: 10.1109/TMI.2012.2187213. View

19.

Li S, Fang L, Yin H . An efficient dictionary learning algorithm and its application to 3-D medical image denoising. IEEE Trans Biomed Eng. 2011; 59(2):417-27. DOI: 10.1109/TBME.2011.2173935. View

20.

Zhang H, Ma J, Wang J, Liu Y, Han H, Lu H . Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach. Comput Med Imaging Graph. 2015; 43:26-35. PMC: 4450134. DOI: 10.1016/j.compmedimag.2015.02.008. View