Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets

Deep learning has attracted growing interest for application to medical imaging, such as positron emission tomography (PET), due to its excellent performance. Convolutional neural networks (CNNs), a facet of deep learning requires large training-image datasets. This presents a challenge in a clinica...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fumio Hashimoto [verfasserIn] Hiroyuki Ohba [verfasserIn] Kibo Ote [verfasserIn] Atsushi Teramoto [verfasserIn] Hideo Tsukada [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Convolutional neural networks deep image prior deep learning denoising dynamic positron emission tomography

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 96594-96603
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:96594-96603

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2929230

Katalog-ID:	DOAJ05768474X

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callnumber-first	T - Technology
author	Fumio Hashimoto
spellingShingle	Fumio Hashimoto misc TK1-9971 misc Convolutional neural networks misc deep image prior misc deep learning misc denoising misc dynamic positron emission tomography misc Electrical engineering. Electronics. Nuclear engineering Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets
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topic_title	TK1-9971 Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets Convolutional neural networks deep image prior deep learning denoising dynamic positron emission tomography
topic	misc TK1-9971 misc Convolutional neural networks misc deep image prior misc deep learning misc denoising misc dynamic positron emission tomography misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Convolutional neural networks misc deep image prior misc deep learning misc denoising misc dynamic positron emission tomography misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Convolutional neural networks misc deep image prior misc deep learning misc denoising misc dynamic positron emission tomography misc Electrical engineering. Electronics. Nuclear engineering
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title	Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets
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title_full	Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets
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title_sort	dynamic pet image denoising using deep convolutional neural networks without prior training datasets
callnumber	TK1-9971
title_auth	Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets
abstract	Deep learning has attracted growing interest for application to medical imaging, such as positron emission tomography (PET), due to its excellent performance. Convolutional neural networks (CNNs), a facet of deep learning requires large training-image datasets. This presents a challenge in a clinical setting because it is difficult to prepare large, high-quality patient-related datasets. Recently, the deep image prior (DIP) approach has been devised, based on the fact that CNN structures have the intrinsic ability to solve inverse problems such as denoising without pre-training and do not require the preparation of training datasets. Herein, we proposed the dynamic PET image denoising using a DIP approach, with the PET data itself being used to reduce the statistical image noise. Static PET data were acquired for input to the network, with the dynamic PET images being handled as training labels, while the denoised dynamic PET images were represented by the network output. We applied the proposed DIP method to computer simulations and also to real data acquired from a living monkey brain with <sup<18</sup<F-fluoro-2-deoxy-D-glucose (<sup<18</sup<F-FDG). As a simulation result, our DIP method produced less noisy and more accurate dynamic images than the other algorithms. Moreover, using real data, the DIP method was found to perform better than other types of post-denoising method in terms of contrast-to-noise ratio, and also maintain the contrast-to-noise ratio when resampling the list data to 1/5 and 1/10 of the original size, demonstrating that the DIP method could be applied to low-dose PET imaging. These results indicated that the proposed DIP method provides a promising means of post-denoising for dynamic PET images.
abstractGer	Deep learning has attracted growing interest for application to medical imaging, such as positron emission tomography (PET), due to its excellent performance. Convolutional neural networks (CNNs), a facet of deep learning requires large training-image datasets. This presents a challenge in a clinical setting because it is difficult to prepare large, high-quality patient-related datasets. Recently, the deep image prior (DIP) approach has been devised, based on the fact that CNN structures have the intrinsic ability to solve inverse problems such as denoising without pre-training and do not require the preparation of training datasets. Herein, we proposed the dynamic PET image denoising using a DIP approach, with the PET data itself being used to reduce the statistical image noise. Static PET data were acquired for input to the network, with the dynamic PET images being handled as training labels, while the denoised dynamic PET images were represented by the network output. We applied the proposed DIP method to computer simulations and also to real data acquired from a living monkey brain with <sup<18</sup<F-fluoro-2-deoxy-D-glucose (<sup<18</sup<F-FDG). As a simulation result, our DIP method produced less noisy and more accurate dynamic images than the other algorithms. Moreover, using real data, the DIP method was found to perform better than other types of post-denoising method in terms of contrast-to-noise ratio, and also maintain the contrast-to-noise ratio when resampling the list data to 1/5 and 1/10 of the original size, demonstrating that the DIP method could be applied to low-dose PET imaging. These results indicated that the proposed DIP method provides a promising means of post-denoising for dynamic PET images.
abstract_unstemmed	Deep learning has attracted growing interest for application to medical imaging, such as positron emission tomography (PET), due to its excellent performance. Convolutional neural networks (CNNs), a facet of deep learning requires large training-image datasets. This presents a challenge in a clinical setting because it is difficult to prepare large, high-quality patient-related datasets. Recently, the deep image prior (DIP) approach has been devised, based on the fact that CNN structures have the intrinsic ability to solve inverse problems such as denoising without pre-training and do not require the preparation of training datasets. Herein, we proposed the dynamic PET image denoising using a DIP approach, with the PET data itself being used to reduce the statistical image noise. Static PET data were acquired for input to the network, with the dynamic PET images being handled as training labels, while the denoised dynamic PET images were represented by the network output. We applied the proposed DIP method to computer simulations and also to real data acquired from a living monkey brain with <sup<18</sup<F-fluoro-2-deoxy-D-glucose (<sup<18</sup<F-FDG). As a simulation result, our DIP method produced less noisy and more accurate dynamic images than the other algorithms. Moreover, using real data, the DIP method was found to perform better than other types of post-denoising method in terms of contrast-to-noise ratio, and also maintain the contrast-to-noise ratio when resampling the list data to 1/5 and 1/10 of the original size, demonstrating that the DIP method could be applied to low-dose PET imaging. These results indicated that the proposed DIP method provides a promising means of post-denoising for dynamic PET images.
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title_short	Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets
url	https://doi.org/10.1109/ACCESS.2019.2929230 https://doaj.org/article/d4701094f5b94878b8831baaf553f1e4 https://ieeexplore.ieee.org/document/8764327/ https://doaj.org/toc/2169-3536
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author2	Hiroyuki Ohba Kibo Ote Atsushi Teramoto Hideo Tsukada
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doi_str	10.1109/ACCESS.2019.2929230
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up_date	2024-07-03T13:29:52.438Z
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Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets

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