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The number of nuclear spins aligned in the lower energy state is slightly greater than those in the higher energy state. In the presence of an external magnetic field, as the spins precess at the Larmor frequency, the excess population results in a net magnetization oriented along the z axis. When a pulse or a short burst of radio waves at the Larmor frequency is applied along the x axis, the coupling of frequencies causes resonance and flips the nuclear spins of the excess population from the...
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Phase Contrast Magnetic Resonance Imaging in the Rat Common Carotid Artery
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Radiomics Features in Contrast-Enhanced and Nonenhanced Magnetic Resonance Imaging Images Are Associated With High

Sricharan S Veeturi1,2,3, Ashrita Raghuram4, Jacob Miller4

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Radiomics analysis of magnetic resonance imaging (MRI) can accurately identify high-risk intracranial aneurysms (IAs) by quantifying aneurysm wall enhancement. This imaging biomarker approach aids in risk stratification for better patient management.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Pathology

Background:

  • Aneurysm wall enhancement on MRI is a potential biomarker for intracranial aneurysm (IA) risk stratification.
  • MRI signal texture variations may reveal IA wall pathobiology.
  • Radiomics quantifies textural complexity in MRI, aiding IA understanding and risk assessment.

Purpose of the Study:

  • Investigate radiomics from nonenhanced and contrast-enhanced MRI to identify high-risk IAs.
  • Evaluate radiomics model performance across different MRI datasets (3-T, 7-T, external).

Main Methods:

  • Extracted radiomics features from 126 IAs across nonenhanced and contrast-enhanced MRI.
  • Developed a random forest model using 3-T data, incorporating the PHASES score.
  • Validated the model on same-site 3-T, 7-T, and external 3-T datasets.

Main Results:

  • Identified 75 significant radiomics features differentiating high-risk from low-risk IAs.
  • Achieved high performance on the 3-T dataset (90% accuracy, 86% sensitivity, 92% specificity).
  • Observed moderate performance on external datasets (88% accuracy on 3-T, 62% on 7-T).

Conclusions:

  • Radiomics from MRI effectively quantifies aneurysm wall enhancement.
  • This method shows high accuracy for identifying high-risk IAs within the same dataset.
  • Potential as a tool for IA risk stratification and management.