Related Experiment Video
Updated: Aug 20, 2026
Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery
Published on: September 5, 2018
Vascular segmentation of phase contrast magnetic resonance angiograms based on statistical mixture modeling and local
Albert C S Chung1, J Alison Noble, Paul Summers
1Department of Computer Science, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. achung@cs.ust.hk
Abstract:
In this paper, we present an approach to segmenting the brain vasculature in phase contrast magnetic resonance angiography (PC-MRA). According to our prior work, we can describe the overall probability density function of a PC-MRA speed image as either a Maxwell-uniform (MU) or Maxwell-Gaussian-uniform (MGU) mixture model. An automatic mechanism based on Kullback-Leibler divergence is proposed for selecting between the MGU and MU models given a speed image volume. A coherence measure, namely local phase coherence (LPC), which incorporates information about the spatial relationships between neighboring flow vectors, is defined and shown to be more robust to noise than previously described coherence measures. A statistical measure from the speed images and the LPC measure from the phase images are combined in a probabilistic framework, based on the maximum a posteriori method and Markov random fields, to estimate the posterior probabilities of vessel and background for classification. It is shown that segmentation based on both measures gives a more accurate segmentation than using either speed or flow coherence information alone. The proposed method is tested on synthetic, flow phantom and clinical datasets. The results show that the method can segment normal vessels and vascular regions with relatively low flow rate and low signal-to-noise ratio, e.g., aneurysms and veins.
Insights
This study introduces a novel method for segmenting brain vasculature in phase contrast magnetic resonance angiography (PC-MRA) by combining speed and local phase coherence measures for improved accuracy.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Computational Neuroscience
Background:
- Accurate segmentation of brain vasculature in PC-MRA is crucial for diagnosing cerebrovascular diseases.
- Existing methods often struggle with noise and low flow rate regions, limiting their clinical applicability.
Purpose of the Study:
- To develop an automated and robust method for segmenting brain vasculature in PC-MRA.
- To improve segmentation accuracy by integrating speed and flow coherence information.
Main Methods:
- Utilizing Maxwell-uniform (MU) and Maxwell-Gaussian-uniform (MGU) mixture models for PC-MRA speed images.
- Implementing an automatic model selection mechanism using Kullback-Leibler divergence.
- Defining a robust local phase coherence (LPC) measure incorporating spatial flow vector relationships.
- Combining statistical speed measures and LPC within a probabilistic framework (maximum a posteriori, Markov random fields) for vessel/background classification.
Main Results:
- The proposed method demonstrates superior segmentation accuracy compared to using speed or coherence information alone.
- The approach effectively segments normal vessels and challenging vascular regions with low flow rate and low signal-to-noise ratio.
- Successful validation on synthetic, flow phantom, and clinical datasets.
Conclusions:
- The integrated approach of combining speed and LPC measures offers a more accurate and robust solution for PC-MRA brain vasculature segmentation.
- This method holds promise for improved diagnosis and monitoring of cerebrovascular conditions, particularly in challenging cases like aneurysms and veins.
