22 citations · 23 across the 5 of their papers we have counts for
5 papers
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)-…
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…
A quantitative assessment of Geant4 for predicting the yield and distribution of positron-emitting fragments in ion beam therapy
Andrew Chacon, Harley Rutherford, Akram Hamato +14
Purpose: To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting frag…
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…