biostudies-literatureJimenez-Pastor AConselleria de Cultura, Educación y Ciencia, Generalitat ValencianaJoan Rodés1https://www.ebi.ac.uk/biostudies/studies/S-EPMC12770010biostudies-literatureUnknown10(1)<h4>Objectives</h4>This study investigated the influence of hepatic vessels on the quantification of magnetic resonance imaging (MRI) proton density fat fraction (PDFF) and R2* using automated whole-liver segmentation.<h4>Materials and methods</h4>This prospective multicenter study included patients with chronic liver disease having paired liver biopsy and MR exams with a standardized multiecho chemical-shift gradient echo sequence. Automated whole-liver segmentation was performed, generating two masks per patient, one including and the other excluding the major hepatic vessels. PDFF and R2* were quantified and graded for both masks. Histological grading of hepatic steatosis and iron overload severity was used as a reference standard.<h4>Results</h4>A total of 377 patients were evaluated, of whom 54% had hepatic steatosis and 20% had iron overload on biopsy readings. Stratified by histological grades, there were no statistically significant differences in the distribution of PDFF or R2* between the two segmentation masks. Overall, PDFF and R2* values were minimally lower when vessels were included, with a bias of -0.06% for PDFF and -0.25 s^-1 for R2*. A lower coefficient of variation was obtained for both imaging parameters after excluding vessels. Patients were classified in the same PDFF grades despite the segmentation approach, and only 7 cases (1.9% of the study population) were reclassified for R2* grading, all being upgraded after vessel exclusion.<h4>Conclusion</h4>Excluding hepatic vessels entails nonsignificant differences in PDFF and R2* quantification. Although with limited impact, vessel exclusion improves biomarker precision in research settings demanding high accuracy and increases clinicians' confidence when using automatic tools in clinical practice.<h4>Relevance statement</h4>Fat and iron quantification on MRI are key imaging biomarkers for the accurate non-invasive assessment of patients with chronic liver disease. Proton density, fat fraction, and R2* quantification show minimal differences if hepatic vessels are included or excluded from the liver segmentation mask.<h4>Key points</h4>The effect of hepatic vessels on proton density, fat fraction, and R2* quantification was evaluated. No significant differences were found, excluding hepatic vessels, although their inclusion showed a small negative bias. Vessel exclusion may improve clinicians' confidence and precision in high-sensitivity applications.European radiology experimentalImpact of hepatic vessels on whole liver proton density fat fraction and R2* quantification.PMC12770010JR22/00002CIGE/2022/37Perez-Girbes AMarti-Aguado DAlfaro-Cervello CPereira BMarti-Bonmati LJimenez-Pastor AAlberich-Bayarri AfalseImpact of hepatic vessels on whole liver proton density fat fraction and R2* quantification.<h4>Objectives</h4>This study investigated the influence of hepatic vessels on the quantification of magnetic resonance imaging (MRI) proton density fat fraction (PDFF) and R2* using automated whole-liver segmentation.<h4>Materials and methods</h4>This prospective multicenter study included patients with chronic liver disease having paired liver biopsy and MR exams with a standardized multiecho chemical-shift gradient echo sequence. Automated whole-liver segmentation was performed, generating two masks per patient, one including and the other excluding the major hepatic vessels. PDFF and R2* were quantified and graded for both masks. Histological grading of hepatic steatosis and iron overload severity was used as a reference standard.<h4>Results</h4>A total of 377 patients were evaluated, of whom 54% had hepatic steatosis and 20% had iron overload on biopsy readings. Stratified by histological grades, there were no statistically significant differences in the distribution of PDFF or R2* between the two segmentation masks. Overall, PDFF and R2* values were minimally lower when vessels were included, with a bias of -0.06% for PDFF and -0.25 s^-1 for R2*. A lower coefficient of variation was obtained for both imaging parameters after excluding vessels. Patients were classified in the same PDFF grades despite the segmentation approach, and only 7 cases (1.9% of the study population) were reclassified for R2* grading, all being upgraded after vessel exclusion.<h4>Conclusion</h4>Excluding hepatic vessels entails nonsignificant differences in PDFF and R2* quantification. Although with limited impact, vessel exclusion improves biomarker precision in research settings demanding high accuracy and increases clinicians' confidence when using automatic tools in clinical practice.<h4>Relevance statement</h4>Fat and iron quantification on MRI are key imaging biomarkers for the accurate non-invasive assessment of patients with chronic liver disease. Proton density, fat fraction, and R2* quantification show minimal differences if hepatic vessels are included or excluded from the liver segmentation mask.<h4>Key points</h4>The effect of hepatic vessels on proton density, fat fraction, and R2* quantification was evaluated. No significant differences were found, excluding hepatic vessels, although their inclusion showed a small negative bias. Vessel exclusion may improve clinicians' confidence and precision in high-sensitivity applications.2026-01-01T00:00:00Z2026 Jan2026-06-06T10:54:25.855Z2026-05-29T03:12:22.232ZS-EPMC127700104149112910.1186/s41747-025-00663-1