What type of study is this?

This is a Quantitative Study study.

September 17, 2025

A Physics-Informed Convolutional Neural Network to Estimate Intravoxel Incoherent Motion Parameters in the Liver

Puntos clave

The CNN improved repeatability for D* and D estimates, achieving wCVs of 9.11% and 6.07%, respectively.
Compared to conventional methods, the CNN significantly reduced variability in IVIM parameters related to liver fibrosis.
The study involved training on 338 images from a cohort and testing on 12 subjects over 12 weeks.
Though improvements were noted, the capacity of IVIM parameters to differentiate fibrosis stages may still be limited.

Resumen

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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Cite This Study

Brown et al. (Tue,) studied this question.

synapsesocial.com/papers/68d4597031b076d99fa5c7ba https://doi.org/https://doi.org/10.58530/2025/5141

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