50 citations · 186 across the 11 of their papers we have counts for
11 papers
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…
Machine learning enables experimental access to photon-by-photon arrival times in scintillation detectors
Yuya Onishi, Ryosuke Ota, Fumio Hashimoto +4
Scintillation detectors with excellent timing resolution enable more precise localization of radiation sources in positron emission tomography, leading to substantial improvements…
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)-…
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…
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 (…
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…
Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising
Yuya Onishi, Fumio Hashimoto, Kibo Ote +2
Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration, enabling represent implicit prior using only convolutional neural netw…
Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm
Fumio Hashimoto, Yuya Onishi, Kibo Ote +2
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…
List-Mode PET Image Reconstruction Using Deep Image Prior
Kibo Ote, Fumio Hashimoto, Yuya Onishi +2
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-fligh…
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-…
Direct PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model
Fumio Hashimoto, Kibo Ote
Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction. In particular, CNN-based direct PET i…