{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:07:49Z","timestamp":1786122469816,"version":"build-2736575974"},"reference-count":28,"publisher":"IOP Publishing","issue":"15","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang","award":["2024R01001"],"award-info":[{"award-number":["2024R01001"]}]},{"name":"Zhejiang Provincial 'Jianbing Lingyan+X\u2019","award":["2025C01127"],"award-info":[{"award-number":["2025C01127"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62427807"],"award-info":[{"award-number":["62427807"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,8,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    To enhance the image quality in time-of-flight positron emission tomography (TOF-PET) reconstruction by improving the accuracy of TOF estimation through an unsupervised waveform reshaping method based on cycle-consistent generative adversarial networks (CycleGANs). This method aims to transform distorted waveforms acquired from real detector responses into clean, virtual waveforms generated by Geant4 simulations, thereby significantly improving the signal-to-noise ratio in PET imaging.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We developed a real event signal dataset using a\n                    <jats:sup>22<\/jats:sup>\n                    Na gamma source and LYSO\u2013silicon photomultiplier detector pairs, and proposed an unsupervised waveform reshaping method based on CycleGAN. The framework learns to map experimentally acquired waveforms\u2014distorted by noise, electronic delays, and scintillator response broadening\u2014to clean, virtual waveforms generated by Geant4 simulations that model ideal detector responses. By using cycle-consistency constraints, the model performs this transformation without requiring paired or labeled data. The reshaped waveforms retain essential timing characteristics while reducing distortions, thereby enabling more accurate TOF estimation through standard timing pick-off methods such as constant fraction discrimination (CFD).\n                    <jats:italic>Main results.<\/jats:italic>\n                    After applying CFD timing to the reshaped waveforms, the results show a coincidence time resolution improvement of 41 ps (15.1%) over conventional CFD applied to raw signals, and 81 ps (26.0%) over leading edge discrimination. Moreover, the proposed method achieves lower edge bias compared to supervised convolutional neural network-based approaches.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Our unsupervised TOF enhancement framework eliminates the need for large-scale labeled datasets and reduces hardware dependency by using simulated virtual waveforms. The CycleGAN-based architecture enables efficient, generalizable performance across source positions, offering a practical and scalable strategy for advancing high-precision TOF-PET systems.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae8ca1","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:55:29Z","timestamp":1784328929000},"page":"155026","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised waveform reshaping for enhanced time-of-flight precision in PET detectors"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9254-3668","authenticated-orcid":true,"given":"Chengkai","family":"He","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhui","family":"Feng","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Jin","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8394-9624","authenticated-orcid":true,"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9715-4636","authenticated-orcid":true,"given":"Yuya","family":"Onishi","sequence":"additional","affiliation":[{"name":"Hamamatsu Photonics K.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6345-1982","authenticated-orcid":true,"given":"Ryosuke","family":"Ota","sequence":"additional","affiliation":[{"name":"Hamamatsu Photonics K.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2093-8771","authenticated-orcid":true,"given":"Min","family":"Guo","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9737-9437","authenticated-orcid":true,"given":"Huafeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering & Instrument Science, Zhejiang University"},{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"pmbae8ca1bib1","doi-asserted-by":"publisher","first-page":"02LT01","DOI":"10.1088\/1361-6560\/aa9dc5","type":"journal-article","article-title":"Using convolutional neural networks to estimate time-of-flight from PET detector waveforms","volume":"63","author":"Berg","year":"2018","journal-title":"Phys. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-02-24","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-17","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-07","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}