{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tamada D"],"funding":["NIBIB NIH HHS","NIDDK NIH HHS","National Institutes of Health","GE Healthcare","NIH HHS"],"pagination":["2172-2187"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10950533"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["91(5)"],"pubmed_abstract":["<h4>Purpose</h4>The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) and R2*$$ {R}_2^{\\ast } $$ maps of the liver, generated with chemical shift-encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms.<h4>Methods</h4>Confidence maps for both PDFF and R2*$$ {R}_2^{\\ast } $$ maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF and R2*$$ {R}_2^{\\ast } $$ analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF and R2*$$ {R}_2^{\\ast } $$ measurements.<h4>Results</h4>Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and R2*$$ {R}_2^{\\ast } $$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and R2*$$ {R}_2^{\\ast } $$ maps, as identified by confidence maps.<h4>Conclusion</h4>A proposed confidence map algorithm that identifies reliable areas of PDFF and R2*$$ {R}_2^{\\ast } $$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility."],"journal":["Magnetic resonance in medicine"],"pubmed_title":["Confidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver."],"pmcid":["PMC10950533"],"funding_grant_id":["R01 DK088925","R01-EB031886","R01‐DK088925","R01 EB031886","R44 EB025729","R01‐DK117354","R01 DK100651","R41‐EB025729","R01-DK117354","R01-DK088925","R01-DK100651","R41-EB025729","R01‐EB031886","R01‐DK100651","R01 DK117354","R44‐EB025729","R41 EB025729","R44-EB025729"],"pubmed_authors":["van der Heijden RA","Tamada D","Weaver J","Hernando D","Reeder SB"],"additional_accession":[]},"is_claimable":false,"name":"Confidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver.","description":"<h4>Purpose</h4>The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) and R2*$$ {R}_2^{\\ast } $$ maps of the liver, generated with chemical shift-encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms.<h4>Methods</h4>Confidence maps for both PDFF and R2*$$ {R}_2^{\\ast } $$ maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF and R2*$$ {R}_2^{\\ast } $$ analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF and R2*$$ {R}_2^{\\ast } $$ measurements.<h4>Results</h4>Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and R2*$$ {R}_2^{\\ast } $$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and R2*$$ {R}_2^{\\ast } $$ maps, as identified by confidence maps.<h4>Conclusion</h4>A proposed confidence map algorithm that identifies reliable areas of PDFF and R2*$$ {R}_2^{\\ast } $$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2025-07-12T03:04:22.88Z","creation":"2025-07-12T03:04:22.88Z"},"accession":"S-EPMC10950533","cross_references":{"pubmed":["38174431"],"doi":["10.1002/mrm.29986"]}}