Dataset Information

PET image denoising using unsupervised deep learning.

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

SUBMITTER: Cui J

PROVIDER: S-EPMC7814987 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

PET image denoising using unsupervised deep learning.

Cui Jianan J Gong Kuang K Guo Ning N Wu Chenxi C Meng Xiaxia X Kim Kyungsang K Zheng Kun K Wu Zhifang Z Fu Liping L Xu Baixuan B Zhu Zhaohui Z Tian Jiahe J Liu Huafeng H Li Quanzheng Q

European journal of nuclear medicine and molecular imaging 20190829 13

<h4>Purpose</h4>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.<h4>Methods</h4>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. Constra ...[more]

PMID: 31468181

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Dataset Information

PET image denoising using unsupervised deep learning.

Purpose

Methods

Results

Publications

PET image denoising using unsupervised deep learning.

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