医用画像情報学会雑誌
Online ISSN : 1880-4977
Print ISSN : 0910-1543
ISSN-L : 0910-1543
研究速報
Unsupervised Dynamic PET Image Denoising with Anatomical Information
Fumio HASHIMOTOHiroyuki OHBAKibo OTEAtsushi TERAMOTO
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ジャーナル 認証あり

2020 年 37 巻 3 号 p. 58-61

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In this paper, we propose an unsupervised dynamic positron emission tomography (PET) image denoising scheme using a deep image prior with anatomical information as convolutional neural network input. The proposed conditional DIP method is an unsupervised deep learning technique with no need to prepare any prior training of image datasets including high- and low-quality image pairs, and only learns using a single data pair of a target dynamic PET image and the anatomical information of a patient’s own magnetic resonance image. A numerical simulation is performed using a three-dimensional brain phantom with 18F-FDG kinetics. Results show that using the anatomical information, the proposed conditional DIP method yielded improved image quality and quantitative performance.

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© 2020 by Japan Society of Medical Imaging and Information Sciences
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