Keywords: AI Diffusion Models, IVIM, Analysis/Processing, Biomarkers, Body, Diabetes, Diffusion Reconstruction, Metabolism, Quantitative Imaging
Motivation: Intravoxel incoherent motion (IVIM) MRI produces diffusion estimates related to stages of liver fibrosis, however, there is an overlap of values between fibrosis stages and poor repeatability.
Goal(s): We aimed to improve repeatability using a convolutional neural network (CNN) for the estimation of IVIM parameters.
Approach: A CNN was trained on 338 images from the San Antonio Mexican American Family Study cohort and tested on 12 subjects at baseline and 12-week follow-up.
Results: The CNN demonstrated improved repeatability for D* (wCV: 9.11% v. 19.3%) and D (wCV: 6.07% v. 10.7%) compared to the conventional non-linear least squares method.
Impact: This study showed that CNNs improve the repeatability of D* and D estimates in the liver, though it remains unclear if the within-subject variability of IVIM parameters is sufficient to accurately differentiate fibrosis stages.
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