most citedAnatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance

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physics.med-ph2026

Multi-Kernel TOF-PET Image Reconstruction Using ADMM

Kibo Ote, Fumio Hashimoto, Yuya Onishi +3

Time-of-flight positron emission tomography (TOF-PET) detectors exhibiting multiple coincidence time resolution (CTR) components, such as those induced by the mixing of Cherenkov a…

physics.med-ph20251 cited

Exploiting network optimization stability for enhanced PET image denoising using deep image prior

Fumio Hashimoto, Kibo Ote, Yuya Onishi +5

PET is affected by statistical noise due to constraints on tracer dose and scan duration, impacting both diagnostic performance and quantitative accuracy. While deep learning (DL)-…

physics.med-ph2024

List-Mode PET Image Reconstruction Using Dykstra-Like Splitting

Kibo Ote, Fumio Hashimoto, Yuya Onishi +1

Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging t…

physics.med-ph2023

ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection

Fumio Hashimoto, Kibo Ote

[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 (…

physics.med-ph2023

Emphasizing Cherenkov photons from bismuth germanate by single photon deconvolution

Ryosuke Ota, Kibo Ote

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 prope…

physics.med-ph202150 cited

Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance

Yuya Onishi, Fumio Hashimoto, Kibo Ote +4

Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low-…