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Related Experiment Video

Updated: Jan 19, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction.

Keith A Cauley1, Gino J Mongelluzzo1, Samuel W Fielden2,3

  • 1Department of Radiology, Geisinger, Danville, PA 17821, USA.

International Journal of Biomedical Imaging
|September 19, 2019
PubMed
Summary

Image intensity inhomogeneity correction aids in detecting early ischemic changes on head CT scans. This method improves the identification and volume measurement of early infarcts, crucial for stroke treatment decisions.

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Area of Science:

  • Neuroradiology
  • Medical Imaging Analysis
  • Stroke Imaging

Background:

  • Early ischemic changes (EIC) on noncontrast head CT are vital for stroke treatment but difficult to delineate.
  • Standard volume measurement methods fail due to poor lesion boundary definition in early infarcts.

Purpose of the Study:

  • To test if image intensity inhomogeneity correction (IIC) can sensitively identify subtle hypodensity characteristic of early ischemic infarcts.
  • To develop and evaluate a digital image analysis algorithm using IIC and intensity thresholding for EIC detection.

Main Methods:

  • Developed an algorithm using IIC and intensity thresholding.
  • Compared two IIC algorithms (FSL and ITK).
  • Evaluated the method using simulated infarcts and 30 patients with 41 acute infarcts.

Main Results:

  • Simulated infarcts showed strong correlation between measured and true volumes (Pearson r = 0.998).
  • Accuracy improved with larger lesion size and lower density.
  • In clinical cases, FSL IIC volumes correlated with ASPECTS (r = 0.680) and NIHSS (r = 0.544), and strongly with IV-tPA treatment decisions.

Conclusions:

  • IIC is a sensitive tool for detecting EIC on noncontrast head CT.
  • The developed algorithm aids in infarct detection and volume assessment.
  • IIC can guide improvements in scan quality for optimal EIC detection.