50 citations · 189 across the 12 of their papers we have counts for
12 papers
VLM- and LLM-Driven Multi-Agent System for PET Image Denoising
Boxiao Yu, Savas Ozdemir, Yang Xing +8
Positron emission tomography (PET) imaging suffers from limited spatial resolution and low signal-to-noise ratio, which can compromise quantitative accuracy and lesion detectabilit…
Flow Matching-Based PET Image Reconstruction
Fumio Hashimoto, Ziqian Huang, Tatsuya Yokota +1
Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstruction methods have demonstrated p…
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)-…
Imaging simulation of a dual-panel PET geometry with ultrafast TOF detectors
Taiyo Ishikawa, Go Akamatsu, Hideaki Tashima +7
In positron emission tomography (PET), time-of-flight (TOF) information localizes source positions along lines of response. Cherenkov-radiator-integrated microchannel-plate photomu…
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 (…
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…
Direct positron emission imaging: ultra-fast timing enables reconstruction-free imaging
Ryosuke Ota, Sun Il Kwon, Eric Berg +7
Positron emission tomography, like many other tomographic imaging modalities, relies on an image reconstruction step to produce cross-sectional images from projection data. Detecti…