Confidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver

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 $R_2^{*}$ 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.

Confidence maps for both PDFF and $R_2^{*}$ 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 $R_2^{*}$ 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 $R_2^{*}$ measurements.

Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and $R_2^{*}$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and $R_2^{*}$ maps, as identified by confidence maps.

A proposed confidence map algorithm that identifies reliable areas of PDFF and $R_2^{*}$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.