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Experimental data from eleven phantoms and ten healthy volunteers were included in the study.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In phantom studies, agreement between T<jats:sub>1<\/jats:sub> reference values and those obtained with the PTNN yielded a statistically significant smaller bias than conventional fitting estimates (-26.69\u2009\u00b1\u200929.5ms vs. -65.0\u2009\u00b1\u200933.25ms, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). For in vivo studies, T<jats:sub>1<\/jats:sub> estimates derived from the PTNN yielded higher T<jats:sub>1<\/jats:sub> values (1152.4\u2009\u00b1\u200925.8ms myocardium, 1640.7\u2009\u00b1\u200930.6ms blood) than conventional fitting (1050.8\u2009\u00b1\u200924.7ms myocardium, 1597.2\u2009\u00b1\u200939.9ms blood). For PTNN, shortening the acquisition time by eliminating the pause between inversion pulses yielded higher myocardial T<jats:sub>1<\/jats:sub> values (1162.2\u2009\u00b1\u200919.7ms with pause vs. 1127.1\u2009\u00b1\u200919.7ms, <jats:italic>p<\/jats:italic>\u2009=\u20090.01 myocardium), (1624.7\u2009\u00b1\u200933.9ms with pause vs. 1645.4\u2009\u00b1\u200918.7ms, <jats:italic>p<\/jats:italic>\u2009=\u20090.16 blood). For conventional fitting statistically significant differences were found.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Compared to T<jats:sub>1<\/jats:sub> maps derived by conventional fitting, PTNN is a post-processing method that yielded T<jats:sub>1<\/jats:sub> maps with higher values and better accuracy in phantoms for a physiological range of T<jats:sub>1<\/jats:sub> and T<jats:sub>2<\/jats:sub> values. In normal volunteers PTNN yielded higher T<jats:sub>1<\/jats:sub> values even with a shorter acquisition scheme of eight heartbeats scan time, without deploying new pulse sequences.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12880-025-01769-z","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T12:33:08Z","timestamp":1751373188000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural networks with personalized training for improved MOLLI T1 mapping"],"prefix":"10.1186","volume":"25","author":[{"given":"Olympia","family":"Gkatsoni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos G.","family":"Xanthis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Johansson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Einar","family":"Heiberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H\u00e5kan","family":"Arheden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony H.","family":"Aletras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"1769_CR1","doi-asserted-by":"publisher","first-page":"681","DOI":"10.2459\/JCM.0000000000000275","volume":"16","author":"A Barison","year":"2015","unstructured":"Barison A, del Torto A, Chiappino S, Aquaro GD, Todiere G, Vergaro G, Passino C, Lombardic M, Emdin M, Masci PG. 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