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Magnetic Resonance Imaging01:24

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Related Experiment Video

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A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
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Towards Automated Quantification of Vessel Wall Composition Using MRI.

Magnus Ziegler1,2, Elin Good1,2,3, Jan Engvall1,2,4

  • 1Cardiovascular Sciences, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.

Journal of Magnetic Resonance Imaging : JMRI
|March 11, 2020
PubMed
Summary

This study introduces an automated method for segmenting carotid arteries (CAs) and analyzing plaque composition using MRI data. The automated approach provides accurate fat fraction (FF) and R2* measurements, similar to manual analysis, improving efficiency.

Keywords:
atherosclerosiscarotid arteriescontrast-enhancedmagnetic resonance imagingplaque compositionsegmentation

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Area of Science:

  • Medical Imaging
  • Cardiovascular Research
  • Artificial Intelligence in Medicine

Background:

  • Magnetic Resonance Imaging (MRI) can assess carotid artery (CA) plaque composition, including lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH), using fat fraction (FF) and R2* data.
  • Traditional manual analysis of these MRI data is time-consuming and labor-intensive.

Purpose of the Study:

  • To develop and validate an automated method for segmenting the carotid artery (CA) and extracting vessel wall composition data.
  • To compare the efficiency and accuracy of automated versus manual analysis of CA plaque composition.

Main Methods:

  • Prospective study involving 31 subjects from the Swedish CArdioPulmonary bioImage Study (SCAPIS).
  • Acquisition of T1-weighted (T1W) quadruple inversion recovery, contrast-enhanced MR angiography (CE-MRA), and 4-point Dixon MRI data at 3T.
  • Automated segmentation of the CA lumen using support vector machines (SVM) with CE-MRA, followed by vessel wall delineation. Generation of FF and R2* maps from Dixon data.

Main Results:

  • Automated CA segmentation achieved a Dice score of 0.89 ± 0.02 and a true-positive ratio of 0.93 ± 0.03, with a median visual score of 4/5.
  • Compositional data extracted from 0.5 mm and 1 mm vessel wall regions using automated segmentation showed similarity to manual analysis results.
  • For the 0.5 mm region, mean differences were 0.1 ± 2.5% for FF and 1.1 ± 5.7 [1/s] for R2*.

Conclusions:

  • The developed automated method accurately segments the carotid artery (CA) and extracts plaque composition data (FF and R2*).
  • Automated analysis offers comparable results to manual methods, significantly improving efficiency in characterizing CA plaque features.
  • This automated approach holds promise for faster and more reproducible assessment of cardiovascular risk through plaque analysis.