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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Related Experiment Video

Updated: Mar 3, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Automatic high-resolution infarct detection using volumetric multiphase dual-energy CT.

Veit Sandfort1, Alan C Kwan2, Comfort Elumogo1

  • 1Radiology and Imaging Sciences - National Institutes of Health Clinical Center, Bethesda, MD, USA.

Journal of Cardiovascular Computed Tomography
|April 27, 2017
PubMed
Summary

This study introduces an automated method for detecting myocardial infarcts using CT angiography (CTA) and late contrast enhancement CT (LCE-CT). The novel technique accurately differentiates infarcts from blood pool, improving diagnostic capabilities.

Keywords:
Delayed iodine contrastLate contrast enhancement CTMyocardial infarct

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Area of Science:

  • Cardiovascular Imaging
  • Radiology
  • Medical Technology

Background:

  • Late contrast enhancement CT (LCE-CT) is used to visualize myocardial infarcts.
  • Differentiating contrast-enhanced infarcts from the blood pool on LCE-CT is a significant challenge in cardiac imaging.

Purpose of the Study:

  • To develop and validate a novel automated method for infarct detection on LCE-CT.
  • To leverage data from first-pass CT angiography (CTA) to improve infarct segmentation accuracy.

Main Methods:

  • A canine model of myocardial infarction was used.
  • Co-registration of CTA and LCE-CT images using a non-rigid, B-spline-based method.
  • Automated blood pool segmentation from CTA transferred to LCE-CT for infarct differentiation.

Main Results:

  • The automated method demonstrated excellent agreement with expert segmentation for infarct volume (ICC=0.96) and area (ICC=0.87).
  • Moderate agreement was found with MRI reference standard (ICC=0.62), with excellent correlation (R=0.9).
  • Minimal manual interaction (<1 minute per exam) was required.

Conclusions:

  • An automated method for infarct segmentation on LCE-CT using multiphase CT data was successfully developed.
  • The method shows excellent agreement with expert readers and favorable correlation with MRI.
  • This approach offers a promising solution for accurate and efficient infarct characterization.