biostudies-literatureTamada DNIBIB NIH HHSNIDDK NIH HHSNational Institutes of HealthGE HealthcareNIH HHS2172-2187https://www.ebi.ac.uk/biostudies/studies/S-EPMC10950533biostudies-literatureUnknown91(5)<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.Magnetic resonance in medicineConfidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver.PMC10950533R01 DK088925R01-EB031886R01‐DK088925R01 EB031886R44 EB025729R01‐DK117354R01 DK100651R41‐EB025729R01-DK117354R01-DK088925R01-DK100651R41-EB025729R01‐EB031886R01‐DK100651R01 DK117354R44‐EB025729R41 EB025729R44-EB025729van der Heijden RATamada DWeaver JHernando DReeder SBfalseConfidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver.<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.2024-01-01T00:00:00Z2024 May2025-07-12T03:04:22.88Z2025-07-12T03:04:22.88ZS-EPMC109505333817443110.1002/mrm.29986