{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:32:10Z","timestamp":1779136330791,"version":"3.51.4"},"posted":{"date-parts":[[2024,9,23]]},"group-title":"Radiology and Imaging","reference-count":24,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>[18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) plays a central role in diagnosing and managing cardiac sarcoidosis. We propose a fully automated pipeline for quantification of [18F]FDG PET activity using deep learning (DL) segmentation of cardiac chambers on computed tomography (CT) attenuation maps and evaluate several quantitative approaches based on this framework.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We included consecutive patients undergoing [18F]FDG PET\/CT for suspected cardiac sarcoidosis. DL segmented left atrium, left ventricular(LV), right atrium, right ventricle, aorta, LV myocardium, and lungs from CT attenuation scans. CT-defined anatomical regions were applied to [18F]FDG PET images automatically to target to background ratio (TBR), volume of inflammation (VOI) and cardiometabolic activity (CMA) using full sized and shrunk segmentations.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>A total of 69 patients were included, with mean age of 56.1 \u00b1 13.4 and cardiac sarcoidosis present in 29 (42%). CMA had the highest prediction performance (area under the receiver operating characteristic curve [AUC] 0.919, 95% confidence interval [CI] 0.858 \u2013 0.980) followed by VOI (AUC 0.903, 95% CI 0.834 \u2013 0.971), TBR (AUC 0.891, 95% CI 0.819 \u2013 0.964), and maximum standardized uptake value (AUC 0.812, 95% CI 0.701 \u2013 0.923). Abnormal CMA (\u22651) had a sensitivity of 100% and specificity 65% for cardiac sarcoidosis. Lung quantification was able to identify patients with pulmonary abnormalities.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>We demonstrate that fully automated volumetric quantification of [18F]FDG PET for cardiac sarcoidosis based on CT attenuation map-derived volumetry is feasible, rapid, and has high prediction performance. This approach provides objective measurements of cardiac inflammation with consistent definition of myocardium and background region.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2024.09.20.24314081","type":"posted-content","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T12:35:20Z","timestamp":1727094920000},"source":"Crossref","is-referenced-by-count":1,"title":["AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET\/CT for detection of cardiac sarcoidosis"],"prefix":"10.64898","author":[{"given":"Robert JH","family":"Miller","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aakash","family":"Shanbhag","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna M","family":"Marcinkiewicz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helen","family":"Struble","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hidesato","family":"Fujito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evan","family":"Kransdorf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Kavanagh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joanna X.","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valerie","family":"Builoff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Damini","family":"Dey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel S","family":"Berman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr J","family":"Slomka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2024092609251040000_2024.09.20.24314081v1.1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehad067"},{"key":"2024092609251040000_2024.09.20.24314081v1.2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.021183"},{"key":"2024092609251040000_2024.09.20.24314081v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.hsr.2022.100034"},{"key":"2024092609251040000_2024.09.20.24314081v1.4","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-8167.2008.01417.x"},{"key":"2024092609251040000_2024.09.20.24314081v1.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrthm.2014.03.043"},{"key":"2024092609251040000_2024.09.20.24314081v1.6","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-013-0370-6"},{"key":"2024092609251040000_2024.09.20.24314081v1.7","doi-asserted-by":"crossref","unstructured":"Chareonthaitawee P , Beanlands RS , Chen W , Dorbala S , Miller EJ , Murthy VL , et al. 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