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Classifying intracranial stenosis disease severity from functional MRI data using machine learning.

Spencer L Waddle1, Meher R Juttukonda1, Sarah K Lants1

  • 1Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.

Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism
|May 10, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning and hemodynamic MRI can identify cerebrovascular disease by analyzing blood flow artifacts. This approach uses support vector machine algorithms to detect stenotic territories, improving diagnostic capabilities.

Keywords:
Strokecerebral blood flowcerebrovascular diseasecerebrovascular reactivitymachine learningmoyamoya

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Cerebrovascular Diseases

Background:

  • Non-invasive hemodynamic MRI methods face challenges in cerebrovascular disease patients due to artifacts like delayed blood arrival and reduced microvascular compliance.
  • These artifacts often hinder the accurate assessment of blood flow and tissue perfusion.

Purpose of the Study:

  • To investigate if MRI-derived hemodynamic artifacts can be utilized as novel contrast sources for discriminating stenotic flow territories in patients with cerebrovascular disease.
  • To apply machine learning, specifically support vector machine (SVM) algorithms, to identify these stenotic regions.

Main Methods:

  • Fifty-three patients with intracranial steno-occlusive moyamoya disease underwent catheter angiography, anatomical MRI, cerebral blood flow (CBF)-weighted arterial spin labeling, and cerebrovascular reactivity (CVR)-weighted MRI.
  • Statistical analysis of CBF and CVR parameters (mean, std, 99th percentile) was performed in territories with and without confirmed stenosis (≥70%).
  • SVMs were employed with k-fold cross-validation and receiver-operating-characteristic-area-under-the-curve analysis to assess the discriminatory power of hemodynamic variables for stenotic territories.

Main Results:

  • Cerebral blood flow standard deviation (CBF-std) in the anterior circulation was the best single predictor of stenotic territories, reflecting heterogeneous endovascular signals and prolonged transit times.
  • A combination of CVR delay (CVRDelay-mean) and CBF-std demonstrated high discriminatory performance (specificity=0.67, sensitivity=0.75).
  • These findings indicate that hemodynamic imaging parameters, particularly those related to delayed vascular compliance, are relevant for identifying impaired cerebrovascular function.

Conclusions:

  • Hemodynamic MRI artifacts, when analyzed with machine learning, can serve as valuable indicators for detecting cerebrovascular impairment.
  • The study highlights the potential of integrating advanced imaging techniques with computational algorithms for improved diagnosis of conditions like moyamoya disease.
  • Further research in hemodynamic imaging and machine learning can enhance the identification and characterization of cerebrovascular disease.