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Nuclear Medicine and Molecular Imaging

01-12-2020 | Positron Emission Tomography | Original Article

Self-supervised PET Denoising

Authors: Si Young Yie, Seung Kwan Kang, Donghwi Hwang, Jae Sung Lee

Published in: Nuclear Medicine and Molecular Imaging | Issue 6/2020

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Abstract

Purpose

Early deep-learning-based image denoising techniques mainly focused on a fully supervised model that learns how to generate a clean image from the noisy input (noise2clean: N2C). The aim of this study is to explore the feasibility of the self-supervised methods (noise2noise: N2N and noiser2noise: Nr2N) for PET image denoising based on the measured PET data sets by comparing their performance with the conventional N2C model.

Methods

For training and evaluating the networks, 18F-FDG brain PET/CT scan data of 14 patients was retrospectively used (10 for training and 4 for testing). From the 60-min list-mode data, we generated a total of 100 data bins with 10-s duration. We also generated 40-s-long data by adding four non-overlapping 10-s bins and 300-s-long reference data by adding all list-mode data. We employed U-Net that is widely used for various tasks in biomedical imaging to train and test proposed denoising models.

Results

All the N2C, N2N, and Nr2N were effective for improving the noisy inputs. While N2N showed equivalent PSNR to the N2C in all the noise levels, Nr2N yielded higher SSIM than N2N. N2N yielded denoised images similar to reference image with Gaussian filtering regardless of input noise level. Image contrast was better in the N2N results.

Conclusion

The self-supervised denoising method will be useful for reducing the PET scan time or radiation dose.
Literature
1.
Lehtinen J, Munkberg J, Hasselgren J, Laine S, Karras T, Aittala M, et al. Noise2Noise: learning image restoration without clean data. arXiv. 2018;1803.04189.
2.
Moran N, Schmidt D, Zhong Y, Coady P. Noisier2Noise: learning to denoise from unpaired noisy data. arXiv. 2019;1910.11908.
3.
Teymurazyan A, Riauka T, Jans H-S, Robinson D. Properties of noise in positron emission tomography images reconstructed with filtered-backprojection and row-action maximum likelihood algorithm. J Digit Imaging. 2013;26:447–56.CrossRef
4.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Pro Med Image Comput Comput Assist Interv. 2015:234–41.
5.
Hegazy MAA, Cho MH, Cho MH, Lee SY. U-net based metal segmentation on projection domain for metal artifact reduction in dental CT. Biomed Eng Lett. 2018;9:375–85.CrossRef
6.
Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, et al. Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med. 2019;60:1183–9.CrossRef
7.
Lee MS, Hwang D, Kim HJ, Lee JS. Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry. Sci Rep. 2019;9:10308.CrossRef
8.
Park J, Bae S, Seo S, Park S, Bang J-I, Han JH, et al. Measurement of glomerular filtration rate using quantitative SPECT/CT and deep-learning-based kidney segmentation. Sci Rep. 2019;9:4223.CrossRef
9.
Hwang D, Kim KY, Kang SK, Seo S, Paeng JC, Lee DS, et al. Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning. J Nucl Med. 2018;59:1624–9.CrossRef
10.
11.
Wang Z, Bovik A, Sheikh H, Simoncelli E. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004:600–12.
12.
Wu D, Gong K, Kim K, Li X, Li Q. Consensus neural network for medical imaging denoising with only noisy training samples. Proc Med Image Comput Comput Assist Interv. 2019:741–9.
Metadata
Title
Self-supervised PET Denoising
Authors
Si Young Yie
Seung Kwan Kang
Donghwi Hwang
Jae Sung Lee
Publication date
01-12-2020
Publisher
Springer Singapore
Published in
Nuclear Medicine and Molecular Imaging / Issue 6/2020
Print ISSN: 1869-3474
Electronic ISSN: 1869-3482
DOI
https://doi.org/10.1007/s13139-020-00667-2