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Then, 120 unseen liver mpMRI examinations of patients with primary sclerosing cholangitis or healthy controls were assessed by coregistering the labels to noncontrast and contrast-enhanced T1 and T2 relaxation time maps for optimization and internal testing. The algorithm was externally tested in a segmental and total liver analysis of previously unseen 65 patients with biopsy-proven liver fibrosis and 25 healthy volunteers. Measured relaxation times were compared to manual measurements using intraclass correlation coefficient (ICC) and Wilcoxon test.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Comparison of manual and deep learning-generated segmental areas on different T1 and T2 maps was excellent for segmental (ICC\u2009=\u20090.95\u2009\u00b1\u20090.1; <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and total liver assessment (0.97\u2009\u00b1\u20090.02, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). The resulting median of the differences between automated and manual measurements among all testing populations and liver segments was 1.8\u2009ms for noncontrast T1 (median 835 <jats:italic>versus<\/jats:italic> 842\u2009ms), 2.0\u2009ms for contrast-enhanced T1 (median 518 <jats:italic>versus<\/jats:italic> 519\u2009ms), and 0.3\u2009ms for T2 (median 37 <jats:italic>versus<\/jats:italic> 37\u2009ms).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Automated quantification of liver mpMRI is highly effective across different patient populations, offering excellent reliability for total and segmental T1 and T2 maps. Its scalable, sequence-adaptive design could foster comprehensive clinical decision-making.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Relevance statement<\/jats:title>\n            <jats:p>The proposed automated, sequence-adaptive algorithm for total and segmental analysis of liver mpMRI may be co-registered to any combination of parametric sequences, enabling comprehensive quantitative analysis of liver mpMRI without sequence-specific training.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Key Points<\/jats:title>\n            <jats:p>\n              <jats:list list-type=\"bullet\">\n                <jats:list-item>\n                  <jats:p>A deep learning-based algorithm automatically quantified segmental T1 and T2 relaxation times in liver mpMRI.<\/jats:p>\n                <\/jats:list-item>\n                <jats:list-item>\n                  <jats:p>The two-step approach of segmentation and co-registration allowed to assess arbitrary sequences.<\/jats:p>\n                <\/jats:list-item>\n                <jats:list-item>\n                  <jats:p>The algorithm demonstrated high reliability with manual reader quantification.<\/jats:p>\n                <\/jats:list-item>\n                <jats:list-item>\n                  <jats:p>No additional sequence-specific training is required to assess other parametric sequences.<\/jats:p>\n                <\/jats:list-item>\n                <jats:list-item>\n                  <jats:p>The DL algorithm has the potential to enhance individual liver phenotyping.<\/jats:p>\n                <\/jats:list-item>\n              <\/jats:list>\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1186\/s41747-025-00596-9","type":"journal-article","created":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T14:24:26Z","timestamp":1749911066000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach"],"prefix":"10.1186","volume":"9","author":[{"given":"Lukas","family":"Zbinden","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Erb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damiano","family":"Catucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Doorenbos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leona","family":"Hulbert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annalisa","family":"Berzigotti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Br\u00f6nimann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Ebner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Christe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Verena Carola","family":"Obmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raphael","family":"Sznitman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6146-8238","authenticated-orcid":false,"given":"Adrian Thomas","family":"Huber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"596_CR1","doi-asserted-by":"publisher","first-page":"4308","DOI":"10.1007\/s00330-020-07447-8","volume":"31","author":"VC Obmann","year":"2021","unstructured":"Obmann VC, Berzigotti A, Catucci D et al (2021) T1 mapping of the liver and the spleen in patients with liver fibrosis\u2014does normalization to the blood pool increase the predictive value? 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