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European Journal of Nuclear Medicine and Molecular Imaging

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01-12-2019 | Positron Emission Tomography | Original Article

PET image denoising using unsupervised deep learning

Authors: Jianan Cui, Kuang Gong, Ning Guo, Chenxi Wu, Xiaxia Meng, Kyungsang Kim, Kun Zheng, Zhifang Wu, Liping Fu, Baixuan Xu, Zhaohui Zhu, Jiahe Tian, Huafeng Liu, Quanzheng Li

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 13/2019

Abstract

Purpose

Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsupervised deep learning, where no training pairs are needed.

Methods

In this method, the prior high-quality image from the patient was employed as the network input and the noisy PET image itself was treated as the training label. Constrained by the network structure and the prior image input, the network was trained to learn the intrinsic structure information from the noisy image and output a restored PET image. To validate the performance of the proposed method, a computer simulation study based on the BrainWeb phantom was first performed. A ⁶⁸Ga-PRGD2 PET/CT dataset containing 10 patients and a ¹⁸F-FDG PET/MR dataset containing 30 patients were later on used for clinical data evaluation. The Gaussian, non-local mean (NLM) using CT/MR image as priors, BM4D, and Deep Decoder methods were included as reference methods. The contrast-to-noise ratio (CNR) improvements were used to rank different methods based on Wilcoxon signed-rank test.

Results

For the simulation study, contrast recovery coefficient (CRC) vs. standard deviation (STD) curves showed that the proposed method achieved the best performance regarding the bias-variance tradeoff. For the clinical PET/CT dataset, the proposed method achieved the highest CNR improvement ratio (53.35% ± 21.78%), compared with the Gaussian (12.64% ± 6.15%, P = 0.002), NLM guided by CT (24.35% ± 16.30%, P = 0.002), BM4D (38.31% ± 20.26%, P = 0.002), and Deep Decoder (41.67% ± 22.28%, P = 0.002) methods. For the clinical PET/MR dataset, the CNR improvement ratio of the proposed method achieved 46.80% ± 25.23%, higher than the Gaussian (18.16% ± 10.02%, P < 0.0001), NLM guided by MR (25.36% ± 19.48%, P < 0.0001), BM4D (37.02% ± 21.38%, P < 0.0001), and Deep Decoder (30.03% ± 20.64%, P < 0.0001) methods. Restored images for all the datasets demonstrate that the proposed method can effectively smooth out the noise while recovering image details.

Conclusion

The proposed unsupervised deep learning framework provides excellent image restoration effects, outperforming the Gaussian, NLM methods, BM4D, and Deep Decoder methods.

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Fletcher JW, Djulbegovic B, Soares HP, Siegel BA, Lowe VJ, Lyman GH, et al. Recommendations on the use of 18F-FDG PET in oncology. J Nucl Med. 2008;49:480–508. https://doi.org/10.2967/jnumed.107.047787.CrossRefPubMed

Beyer T, Townsend DW, Brun T, Kinahan PE, Charron M, Roddy R, et al. A combined PET/CT scanner for clinical oncology. J Nucl Med. 2000;41:1369–79.PubMed

Schwaiger M, Ziegler S, Nekolla SG. PET/CT: challenge for nuclear cardiology. J Nucl Med. 2005;46:1664–78.PubMed

Tai YF. Applications of positron emission tomography (PET) in neurology. J Neurol Neurosurg Psychiatry. 2004;75:669–76. https://doi.org/10.1136/jnnp.2003.028175.CrossRefPubMedPubMedCentral

Gong K, Majewski S, Kinahan PE, Harrison RL, Elston BF, Manjeshwar R, et al. Designing a compact high performance brain PET scanner - simulation study. Phys Med Biol. IOP Publishing. 2016;61:3681–97. https://doi.org/10.1088/0031-9155/61/10/3681.CrossRef

Tauber C, Stute S, Chau M, Spiteri P, Chalon S, Guilloteau D, et al. Spatio-temporal diffusion of dynamic PET images. Phys Med Biol. 2011;56:6583–96. https://doi.org/10.1088/0031-9155/56/20/004.CrossRefPubMed

Dutta J, Leahy RM, Li Q. Non-local means denoising of dynamic PET images. Muñoz-Barrutia A, editor. PLoS One 2013;8:e81390. https://doi.org/10.1371/journal.pone.0081390.CrossRefPubMedPubMedCentral

Boussion N, Cheze Le Rest C, Hatt M, Visvikis D. Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging. Eur J Nucl Med Mol Imaging. 2009;36:1064–75. https://doi.org/10.1007/s00259-009-1065-5.CrossRefPubMed

Shidahara M, Ikoma Y, Seki C, Fujimura Y, Naganawa M, Ito H, et al. Wavelet denoising for voxel-based compartmental analysis of peripheral benzodiazepine receptors with 18F-FEDAA1106. Eur J Nucl Med Mol Imaging. 2008;35:416–23. https://doi.org/10.1007/s00259-007-0623-y.CrossRefPubMed

10.

Christian BT, Vandehey NT, Floberg JM, Mistretta CA. Dynamic PET Denoising with HYPR processing. J Nucl Med. 2010;51:1147–54. https://doi.org/10.2967/jnumed.109.073999.CrossRefPubMed

11.

Xu Z, Bagci U, Seidel J, Thomasson D, Solomon J, Mollura DJ. Segmentation based denoising of PET images: an iterative approach via regional means and affinity propagation. Med Image Comput Comput Assist Interv. 2014;17:698–705. https://doi.org/10.1007/978-3-319-10404-1_87.CrossRefPubMedPubMedCentral

12.

Comtat C, Kinahan PE, Fessler JA, Beyer T, Townsend DW, Defrise M, et al. Clinically feasible reconstruction of 3D whole-body PET/CT data using blurred anatomical labels. Phys Med Biol. 2002;47:1–20. https://doi.org/10.1088/0031-9155/47/1/301.CrossRefPubMed

13.

Baete K, Nuyts J, Van Paesschen W, Suetens P, Dupont P. Anatomical-based FDG-PET reconstruction for the detection of hypo-metabolic regions in epilepsy. IEEE Trans Med Imaging. 2004;23:510–9. https://doi.org/10.1109/tmi.2004.825623.CrossRefPubMed

14.

Bowsher JE, Yuan H, Hedlund LW, Turkington TG, Akabani G, Badea A et al. Utilizing MRI information to estimate F18-FDG distributions in rat flank tumors. IEEE Symp Conf Rec Nucl Sci 2004. IEEE; 2004. p. 2488–92. https://doi.org/10.1109/nssmic.2004.1462760.

15.

Chan C, Fulton R, Barnett R, Feng DD, Meikle S. Postreconstruction nonlocal means filtering of whole-body PET with an anatomical prior. IEEE Trans Med Imaging. 2014;33:636–50. https://doi.org/10.1109/tmi.2013.2292881.CrossRefPubMed

16.

Yan J, Lim JCS, Townsend DW. MRI-guided brain PET image filtering and partial volume correction. Phys Med Biol IOP Publishing. 2015;60:961–76. https://doi.org/10.1109/nssmic.2013.6829058.CrossRef

17.

He K, Sun J, Tang X. Guided image filtering. IEEE Trans Pattern Anal Mach Intell. 2013;35:1397–409.CrossRefPubMed

18.

Somayajula S, Panagiotou C, Rangarajan A, Li Q, Arridge SR, Leahy RM. PET image reconstruction using information theoretic anatomical priors. IEEE Trans Med Imaging. 2011;30:537–49. https://doi.org/10.1109/nssmic.2005.1596899.CrossRefPubMed

19.

Tang J, Rahmim A. Bayesian PET image reconstruction incorporating anato-functional joint entropy. Phys Med Biol. 2009;54:7063–75. https://doi.org/10.1109/isbi.2008.4541178.CrossRefPubMedPubMedCentral

20.

Nuyts J. The use of mutual information and joint entropy for anatomical priors in emission tomography. 2007 IEEE Nucl Sci Symp Conf Rec. IEEE; 2007. p. 4149–54. https://doi.org/10.1109/nssmic.2007.4437034.

21.

Song T, Yang F, Chowdhury SR, Kim K, Johnson KA, El Fakhri G, et al. PET image deblurring and super-resolution with an MR-based joint entropy prior. IEEE Trans Comput Imaging. 2019;1. https://doi.org/10.1109/tci.2019.2913287 CrossRefPubMedPubMedCentral

22.

Wang S, Su Z, Ying L, Peng X, Zhu S, Liang F, et al. Accelerating magnetic resonance imaging via deep learning. 2016 IEEE 13th Int Symp Biomed Imaging. IEEE; 2016. p. 514–517. https://doi.org/10.1109/isbi.2016.7493320.

23.

Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, et al. Low-dose CT via convolutional neural network. Biomed Opt Express. 2017;8:679.CrossRefPubMedPubMedCentral

24.

Wu D, Kim K, Fakhri G El, Li Q. A cascaded convolutional neural network for x-ray low-dose CT image denoising 2017.

25.

Gong K, Guan J, Kim K, Zhang X, Yang J, Seo Y, et al. Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans Med Imaging. 2018:1–8. https://doi.org/10.1109/tmi.2018.2869871.CrossRef

26.

Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M, et al. Ultra–low-dose 18 F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs. Radiology. 2019;290:649–56. https://doi.org/10.1148/radiol.2018180940.CrossRefPubMed

27.

Xiang L, Qiao Y, Nie D, An L, Wang Q, Shen D. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing. 2018;406–16. https://doi.org/10.1016/j.neucom.2017.06.048.CrossRefPubMedPubMedCentral

28.

Ulyanov D, Vedaldi A, Lempitsky V. Deep image prior. 2017 IEEE Conf Comput Vis Pattern Recognit. IEEE; 2017; pp. 5882–5891. https://doi.org/10.1109/cvpr.2018.00984.

29.

Mirza M, Osindero S. Conditional generative adversarial nets. Cambridge: Cambridge University Press; 2014. p. 1–7. Available from: http://arxiv.org/abs/1411.1784.

30.

Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2016. pp. 424–32. https://doi.org/10.1007/978-3-319-46723-8_49.CrossRef

31.

Gong K, Kim K, Cui J, Guo N, Catana C, Qi J, et al. Learning personalized representation for inverse problems in medical imaging using deep neural network. 2018;1–11. Available from: http://arxiv.org/abs/1807.01759

32.

Liu DC, Nocedal J. On the limited memory BFGS method for large scale optimization. Math Program. 1989;45:503–28. https://doi.org/10.1007/bf01589116.CrossRef

33.

Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014; Available from: http://arxiv.org/abs/1412.6980.

34.

Nesterov Y. A method for unconstrained convex minimization problem with the rate of convergence o(1/k^2). Dokl AN USSR. 1983;269:543–7.

35.

Cocosco CA, Kollokian V, Kwan RK-S, Pike GB, Evans AC. Brainweb: online interface to a 3D MRI simulated brain database. Citeseer: Neuroimage; 1997.

36.

Maggioni M, Katkovnik V, Egiazarian K, Foi A. Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process. 2013;22:119–33. https://doi.org/10.1109/tip.2012.2210725.CrossRefPubMed

37.

Heckel R, Hand P. Deep decoder: concise image representations from untrained non-convolutional networks. Int Conf Learn Represent. International Conference on Learning Representations; 2019. https://doi.org/10.1109/TIP.2012.2210725.CrossRefPubMed

Title: PET image denoising using unsupervised deep learning
Authors: Jianan Cui
Kuang Gong
Ning Guo
Chenxi Wu
Xiaxia Meng
Kyungsang Kim
Kun Zheng
Zhifang Wu
Liping Fu
Baixuan Xu
Zhaohui Zhu
Jiahe Tian
Huafeng Liu
Quanzheng Li
Publication date: 01-12-2019
Publisher: Springer Berlin Heidelberg
Keyword: Positron Emission Tomography
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 13/2019
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-019-04468-4

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

PET image denoising using unsupervised deep learning

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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