Self-supervised PET Denoising
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ORIGINAL ARTICLE
Self-supervised PET Denoising Si Young Yie 1,2 & Seung Kwan Kang 1,3 & Donghwi Hwang 1,3 & Jae Sung Lee 1,3,4,5 Received: 23 July 2020 / Revised: 22 September 2020 / Accepted: 25 September 2020 # Korean Society of Nuclear Medicine 2020
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 selfsupervised 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. Keywords Positron emission tomography (PET) . Denoising filter . Deep learning . Artificial neural network
Introduction Positron emission tomography (PET) is a widely used medical imaging modality that uses radiotracers to measure various
* Jae Sung Lee [email protected] Si Young Yie [email protected] Seung Kwan Kang [email protected] Donghwi Hwang [email protected] 1
Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, South Korea
2
Department of Mechanical Engineering, Seoul National University College of Engineering, Seoul 08826, South Korea
3
Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, South Korea
4
Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul 03080, South Korea
5
Brightonix Imaging Inc, Seoul 03080, South Korea
physiologic and biochemical processes in the body. However, limited spatial resolution and high noise levels are the main limitations of PET relative to other medical imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI). The noise level and image quality of the PET is mainly dependent on the amount of injected radiotracer and the duration of scanning. Given that the use of a larger amount of radiotracers causes high radiation exposure to patients,
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