What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Fully Automated Segmentation and Hemodynamic Quantification of Carotid Artery 4D Flow MRI

Puntos clave

Good segmentation similarity was achieved with median DSC of 0.88 and 0.89 for left and right carotids.
The algorithm provided close limits of agreement for hemodynamic quantifications, with less than 5% bias in some parameters.
A dense U-net was utilized to train on 265 manually generated labels for segmenting carotids in 4D flow MRI.
This method may enable more reproducible studies of carotid hemodynamics in large cohorts, addressing observer influence.

Resumen

Motivation: Observer influence hampers reproducibility in quantitative analysis. However, there have been relatively few efforts toward creating automated analysis tools for carotid arteries. Goal(s): We aim to investigate and evaluate performance of AI-driven pipeline analysis to automatically segment carotids and quantify hemodynamics from 4D flow MRI. Approach: A dense U-net was trained manually-generated labels (n=265) to segment carotids in 4D flow MRI. Algorithmic analysis quantified three hemodynamic parameters. Segmentation similarity and parameter agreement was evaluated (separate group, n=66). Results: Good segmentation similarity was achieved (median DSC=0.88,0.89 left,right). Close limits of agreement for quantifications were observed, although there was slight (<5%) bias in some parameters. Impact: This study demonstrated a method for automatically generating segmentations of the carotid vessels in 4D flow MR images and quantifying hemodynamics. It can inform efforts to reproducibly study carotid hemodynamics in large cohorts of subjects.

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