Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Ote, Kibo"'
Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging task. The automatic determination of relaxation parameters for list-mod
Externí odkaz:
http://arxiv.org/abs/2403.00394
Autor:
Hashimoto, Fumio, Ote, Kibo
Publikováno v:
Phys. Med. Biol. 69 (2024) 105022
[Objective] This study aims to introduce a novel back projection-induced U-Net-shaped architecture, called ReconU-Net, for deep learning-based direct positron emission tomography (PET) image reconstruction. Additionally, our objective is to analyze t
Externí odkaz:
http://arxiv.org/abs/2312.02494
Autor:
Ota, Ryosuke, Ote, Kibo
Bismuth germanate (BGO) has been receiving attention again because it is a potential scintillator for future time-of-flight positron emission tomography. Owing to its optical properties, BGO emits a relatively large number of Cherenkov photons after
Externí odkaz:
http://arxiv.org/abs/2311.04432
Publikováno v:
IEEE Trans. Radiat. Plasma Med. Sci. 8 (2024) 348
Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration, enabling represent implicit prior using only convolutional neural network architecture without training dataset, whereas the general supervi
Externí odkaz:
http://arxiv.org/abs/2302.13546
Publikováno v:
Phys. Med. Biol. 68 (2023) 155009
Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction, which does not require any prior training dataset. In this paper, we present the first attempt to implement an
Externí odkaz:
http://arxiv.org/abs/2212.11844
Publikováno v:
IEEE Trans. Med. Imaging 42 (2023) 1822
List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response and additional information such as time-of-flight and depth-of-interaction. Deep learning is one possible solution to
Externí odkaz:
http://arxiv.org/abs/2204.13404
Autor:
Onishi, Yuya, Hashimoto, Fumio, Ote, Kibo, Ohba, Hiroyuki, Ota, Ryosuke, Yoshikawa, Etsuji, Ouchi, Yasuomi
Publikováno v:
Med. Image Anal. 74 (2021) 102226
Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low- and high-quality reference PET image pairs. Herein, we propose an unsu
Externí odkaz:
http://arxiv.org/abs/2109.00802
Autor:
Hashimoto, Fumio, Ote, Kibo
Publikováno v:
IEEE Trans. Radiat. Plasma Med. Sci. 6 (2022) 841
Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction. In particular, CNN-based direct PET image reconstruction, which directly generates the reconstructed image
Externí odkaz:
http://arxiv.org/abs/2109.00768
Autor:
Obana, Akira1,2 (AUTHOR) obana@sis.seirei.or.jp, Ote, Kibo3 (AUTHOR), Gohto, Yuko1 (AUTHOR), Yamada, Hidenao3 (AUTHOR), Hashimoto, Fumio3 (AUTHOR), Okazaki, Shigetoshi3 (AUTHOR), Asaoka, Ryo1 (AUTHOR)
Publikováno v:
PLoS ONE. 2/13/2024, Vol. 19 Issue 2, p1-11. 11p.
Publikováno v:
Radiological Physics & Technology; Sep2024, Vol. 17 Issue 3, p776-781, 6p