{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T13:30:35Z","timestamp":1788442235026,"version":"build-2803163510"},"reference-count":40,"publisher":"IOP Publishing","issue":"17","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"the Natural Science Foundation of Guangdong Province in China","award":["2023B1515120007"],"award-info":[{"award-number":["2023B1515120007"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220818101804009"],"award-info":[{"award-number":["JCYJ20220818101804009"]}]},{"name":"the Shenzhen Excellent Technological Innovation Talent Training Project of China","award":["RCJC20200714114436080"],"award-info":[{"award-number":["RCJC20200714114436080"]}]},{"name":"Shenzhen Medical Research Fund of China","award":["B2301002"],"award-info":[{"award-number":["B2301002"]}]},{"name":"the Shenzhen Science and Technology Program of China","award":["JCYJ20220818101804009"],"award-info":[{"award-number":["JCYJ20220818101804009"]}]},{"name":"the National Key Research and Development Program of China","award":["2024YFE0202400"],"award-info":[{"award-number":["2024YFE0202400"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12326607"],"award-info":[{"award-number":["12326607"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Outstanding Talents Training Fund"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,9,7]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n The lengthy 1 h dynamic positron emission tomography (PET) scans discomfort patients, add motion artifacts, and inflate costs, highlighting the need for tech advancements to reduce scan times. Therefore, we attempted to reconstruct multi-parametric imaging with only 20 min dynamic PET frames through deep learning techniques.\n Approach.<\/jats:italic>\n Total-body PET data from 49 subjects (18 [\n 68<\/jats:sup>\n Ga]Ga-FAPI-04 and 31 [\n 68<\/jats:sup>\n Ga]Ga-prostate-specific membrane antigen (PSMA)-11) at Renji Hospital were used. We employed the dynamic PET frames prediction, parametric linear fitting and generative adversarial network to form the whole deep learning framework accomplishing reconstruction of multi-parametric images ([\n 68<\/jats:sup>\n Ga]Ga-FAPI-04:\n \n \n \n <\/jats:tex-math>\n \n \n \n \n K<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , DV and\n \n \n \n <\/jats:tex-math>\n \n \n \n \n v<\/mml:mi>\n \n B<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ; [\n 68<\/jats:sup>\n Ga]Ga-PSMA-11:\n \n \n \n <\/jats:tex-math>\n \n \n \n \n K<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n <\/jats:tex-math>\n \n \n \n \n K<\/mml:mi>\n \n i<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n \n \n \n <\/jats:tex-math>\n \n \n \n \n v<\/mml:mi>\n \n B<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ) from 20 min dynamic PET frames. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were used to evaluate the quality of the synthesized results. Statistical analyses, including Pearson correlation coefficient (PCC) and violin plot, were also used for the reconstruction assessments.\n Main results.<\/jats:italic>\n The reconstructed multi-parametric images using the proposed technique closely resembled their full-time parametric counterparts, demonstrating high image quality across various tissue types. The reconstructed images resulting from the [\n 68<\/jats:sup>\n Ga]Ga-FAPI-04 and [\n 68<\/jats:sup>\n Ga]Ga-PSMA-11 tracers outperform 5 comparison methods, yielding average PSNR values of 35.5535 dB and 36.4040 dB, along with SSIM scores of 0.9798 and 0.9630, respectively. Additionally, the PCCs within the region of interests exhibited strong correlations (above 0.92) with the ground truth. Moreover, the imaging results in the pancreatic and prostate areas matched well with full-time parametric images.\n Significance.<\/jats:italic>\n Our approach shortens the scan duration of dynamic PET from above one hour to 20 min, which greatly improves the experience for patients and reduces motion artifacts and costs.\n <\/jats:p>","DOI":"10.1088\/1361-6560\/adfe33","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T22:54:10Z","timestamp":1755816850000},"page":"175019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Reconstruction of total-body multi parametric images with shortened-duration dynamic [\n 68<\/sup>\n Ga]Ga-PSMA-11 and [\n 68<\/sup>\n Ga]Ga-FAPI-04 PET scans"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4858-3545","authenticated-orcid":false,"given":"Yunlong","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9042-4477","authenticated-orcid":true,"given":"Zixiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2872-599X","authenticated-orcid":false,"given":"Wenjie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1642-6631","authenticated-orcid":true,"given":"Yongfeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8558-5102","authenticated-orcid":false,"given":"Hairong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1358-9777","authenticated-orcid":true,"given":"Dong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruohua","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0618-6240","authenticated-orcid":true,"given":"Zhanli","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"pmbadfe33bib1","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1007\/s12149-024-01939-z","type":"journal-article","article-title":"Dual-time-point dynamic 68Ga-PSMA-11 PET\/CT for parametric imaging generation in prostate cancer","volume":"38","author":"Burasothikul","year":"2024","journal-title":"Ann. 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