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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

Jiang Du¹, Afshin Karimi, Yijing Wu

¹Department of Radiology, University of California, San Diego, San Diego, CA 92103-8226, USA. jiangdu@ucsd.edu

Magnetic Resonance Imaging

|November 16, 2010

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This study introduces an automated algorithm for segmenting arteries and veins in contrast-enhanced magnetic resonance angiography (CE-MRA) images. The method uses advanced analysis to accurately differentiate vessels, improving imaging techniques.

Area of Science:

Medical Imaging
Cardiovascular Imaging
Image Analysis

Background:

Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) offers insights into vascular contrast dynamics.
Vessel segmentation is crucial for analyzing vascular structures but traditionally requires manual intervention.

Purpose of the Study:

To develop and validate an automated algorithm for precise vessel segmentation in CE-MRA.
To integrate this algorithm with a dual-phase dynamic imaging acquisition scheme for enhanced results.

Main Methods:

An automated segmentation algorithm incorporating region of interest (ROI) generation, cross-correlation, and pooled sample covariance matrix analysis was developed.
Iterative thresholding based on contrast arrival and enhancement maps was used for ROI generation.

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A dual-phase dynamic imaging acquisition (low-resolution dynamic phase, high-frequency steady-state phase) was employed.

Main Results:

The algorithm successfully automated the segmentation of arteries and veins using Mahalanobis distances for differentiation.
Combining segmented low-resolution images with high-frequency k-space data produced final high-quality segmented arterial and venous images.
Studies on volunteers and patients confirmed the efficacy of the automated segmentation and dual-phase acquisition.

Conclusions:

The presented automated vessel segmentation algorithm significantly enhances CE-MRA analysis.
The dual-phase dynamic imaging acquisition scheme, coupled with automated segmentation, offers a robust method for cardiovascular imaging.
This technique demonstrates considerable advantages for both research and clinical applications in vascular imaging.