{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:27:45Z","timestamp":1757456865051,"version":"3.38.0"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EJNMMI Phys"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Prior to selective internal radiotherapy of liver tumors, a determination of the lung shunt fraction (LSF) is performed using 99mTc- macroaggregated albumin (99mTc-MAA) injected into the hepatic artery. Most commonly planar but sometimes SPECT\/CT images are acquired upon which regions of interests are drawn manually to define the liver and the lung. The LSF is then calculated by taking the count ratios between these two organs. An accurate estimation of the LSF is necessary to avoid an excessive pulmonary irradiation dose.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>In this study, we propose a computational, semi-automatic approach for LSF calculation from SPECT\/CT scans, based on machine learning 3D segmentation, implemented within TriDFusion (3DF). We retrospectively compared this approach with the LSF calculated using the standard planar approach on 150 patients. Using CT images (from the SPECT\/CT) as a blueprint, the TotalSegmentor machine learning algorithm automatically computes masks for the liver and lungs. Then, the SPECT attenuation-corrected images are fused with the CT and, based on the CT segmentation mask, TriDFusion (3DF) generates volume-of- interest (VOI) regions on the SPECT images. The liver and lung VOIs are further augmented to compensate for breathing motion. Finally, the LSF is calculated using the number of counts in the respective VOIs. Measurements using an anthropomorphic 3D-printed phantom with variable 99mTc activity concentrations for the liver and lungs were performed to validate the accuracy of the algorithm.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>On average, LSF determined from 2D planar images were between 21 and 70% higher than those determined from SPECT\/CT data. Semi-automated determination of the LSF using TriDFusion (3DF) analysis of SPECT-CT acquisitions was within 4\u201312% of the phantom-determined ratio measurements (ground truth).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The utilization of TriDFusion (3DF) AI 3D Lung Shunt is a precise method for quantifying lung shunt fraction (LSF) and is more accurate than planar 2D image-based estimates. By incorporating machine learning segmentation and compensating for breathing motion, the approach underscores the potential of artificial intelligence (AI)-driven techniques to revolutionize pulmonary imaging, providing clinicians with efficient and reliable tools for treatment planning and patient management.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s40658-025-00732-9","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T03:24:55Z","timestamp":1741663495000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine-learning based quantification of lung shunt fraction from 99mTc-MAA SPECT\/CT for selective internal radiation therapy of liver tumors using TriDFusion (3DF)"],"prefix":"10.1186","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2548-1333","authenticated-orcid":false,"given":"Daniel","family":"Lafontaine","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Finn","family":"Augensen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Kesner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raoul","family":"Vincent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assen","family":"Kirov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone","family":"Krebs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heiko","family":"Sch\u00f6der","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John L.","family":"Humm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"732_CR1","doi-asserted-by":"publisher","first-page":"449","DOI":"10.7861\/clinmedicine.17-5-449","volume":"17","author":"FX Sundram","year":"2017","unstructured":"Sundram FX, Buscombe JR. Selective internal radiation therapy for liver tumours. Clin Med. 2017;17:449.","journal-title":"Clin Med"},{"key":"732_CR2","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1007\/s00259-020-04800-3","volume":"47","author":"X Tang","year":"2020","unstructured":"Tang X, Jafargholi Rangraz E, Coudyzer W, Bertels J, Robben D, Schramm G, Deckers W, Maleux G, Baete K, Verslype C, Gooding MJ. Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. Eur J Nucl Med Mol Imaging. 2020;47:2742\u201352.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"732_CR3","doi-asserted-by":"publisher","first-page":"20220470","DOI":"10.1259\/bjr.20220470","volume":"95","author":"P Torkian","year":"2022","unstructured":"Torkian P, Ragulojan R, Woodhead GJ, D\u2019souza D, Flanagan S, Golzaria J, Young S. Lung shunt fraction quantification methods in radioembolization: what you need to know. Br J Radiol. 2022;95:20220470.","journal-title":"Br J Radiol"},{"key":"732_CR4","doi-asserted-by":"publisher","first-page":"7877","DOI":"10.1002\/mp.15303","volume":"48","author":"A Chaichana","year":"2021","unstructured":"Chaichana A, Frey EC, Teyateeti A, Rhoongsittichai K, Tocharoenchai C, Pusuwan P, Jangpatarapongsa K. Automated segmentation of lung, liver, and liver tumors from Tc-99m MAA SPECT\/CT images for Y-90 radioembolization using convolutional neural networks. Med Phys. 2021;48:7877\u201390.","journal-title":"Med Phys"},{"key":"732_CR5","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s13550-023-00996-1","volume":"13","author":"M Stella","year":"2023","unstructured":"Stella M, van Rooij R, Lam MG, de Jong HW, Braat AJ. Automatic healthy liver segmentation for holmium-166 radioembolization dosimetry. 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Nucl Med Commun. 2016;37:983\u20137.","journal-title":"Nucl Med Commun"}],"container-title":["EJNMMI Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40658-025-00732-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40658-025-00732-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40658-025-00732-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T15:10:14Z","timestamp":1741705814000},"score":1,"resource":{"primary":{"URL":"https:\/\/ejnmmiphys.springeropen.com\/articles\/10.1186\/s40658-025-00732-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,11]]},"references-count":14,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["732"],"URL":"https:\/\/doi.org\/10.1186\/s40658-025-00732-9","relation":{},"ISSN":["2197-7364"],"issn-type":[{"value":"2197-7364","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,11]]},"assertion":[{"value":"16 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The work described in this manuscript was performed under the retrospective and process improvement IRB Protocol #16\u20131488 entitled \u201cImage Processing Applications for Medical Imaging Workstations and Systems.\u201d","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"There are no reproduced images or data from another publication in this submitted manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"TriDFusion (3DF) is free, open-source software that has been developed and led by Daniel Lafontaine at Memorial Sloan Kettering Cancer Center. The software may be redistributed and\/or modified under the terms of the GNU General Public License, as published by the Free Software Foundation, either version 3 of the License, or (at your discretion) any later version. It is for \u201ceducational,\u201d \u201cresearch,\u201d and \u201cnot-for-profit\u201d purposes only, which means any noncommercial and nonclinical use. Any contributed computer code may retain the copyright to TriDFusion (3DF) and users are responsible for their use of the code.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Copyright and collaboration"}}],"article-number":"22"}}