Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study - Md Nasir; Yixi Xu; Kyle Hasenstab; Alekhya Yechoor; Rahul Dodhia; William B. Weeks; Juan Lavista Ferres; Guilherme Moura Cunha (PLoS ONE, 2025)
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases makes early diagnosis important. MRI Proton Density Fat Fraction (PDFF) is the most accurate noninvasive method for quantitatively assessing liver fat but is expensive and has limited availability; accurately quantifying liver fat from more accessible and affordable imaging could potentially improve patient care. This proof-of-concept study explores the feasibility of inferring liver MRI-PDFF values from contrast-enhanced computed tomography (CECT) using deep learning. In this retrospective, cross-sectional study, we analyzed data from living liver donor candidates who had concurrent CECT and MRI-PDFF as part of their pre-surgical workup between April 2021 and October 2022. Manual MRI-PDFF analysis was performed following a standard of clinical care protocol and used as ground truth. After liver segmentation and registration, a deep neural network (DNN) with 3D U-Net architecture was trained using CECT images as single channel input and the concurrent MRI-PDFF images as single channel output. We evaluated performance using mean absolute error (MAE) and root mean squared error (RMSE), and mean errors (defined as the mean difference of results of comparator groups), with 95% confidence intervals (CIs). We used Kappa statistics and Bland-Altman plots to assess agreement between DNN-predicted PDFF and ground truth steatosis grades and PDFF values, respectively. The final study cohort was of 94 patients, mean PDFF = 3.8%, range 0.2–22.3%. When comparing ground truth to segmented reference (MRI-PDFF), our model had an MAE of 0.56, an RMSE of 0.77, and a mean error of 0.06 (−1.75,1.86); when comparing medians of the predicted and reference MRI-PDFF images, our model had an MAE, an RMSE, and a mean error of 2.94, 4.27, and 1.28 (−4.58,7.14), respectively. We found substantial agreement between categorical steatosis grades obtained from DNN-predicted and clinical ground truth PDFF (kappa = 0.75). While its ability to infer exact MRI-PDFF values from CECT images was limited, categorical classification of fat fraction at lower grades was robust, outperforming other prior attempted methods.