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Related Concept Videos

Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
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AUTOMATIC QUANTIFICATION OF TREE-IN-BUD PATTERNS FROM CT SCANS.

Ulas Bagci1, Kirsten Miller-Jaster1, Jianhua Yao2

  • 1Center for Infectious Diseases Imaging, National Institutes of Health (NIH) ; Department of Radiology and Imaging Sciences (NIH).

Proceedings. IEEE International Symposium on Biomedical Imaging
|January 21, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method to quantify Tree-in-Bud (TIB) patterns in CT scans for respiratory infections. The system accurately measures affected lung volume, aiding in disease severity assessment.

Keywords:
CADCTLungQuantificationTree-in-Bud

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Area of Science:

  • Radiology
  • Medical Imaging
  • Pulmonology

Background:

  • Tree-in-Bud (TIB) patterns are indicative of respiratory tract infections.
  • Quantifying TIB is crucial for assessing disease severity but is challenging due to complex morphology and intensity variations.
  • Previous work established a computer-assisted detection (CAD) system for TIB patterns.

Purpose of the Study:

  • To develop and validate a fully automatic method for quantifying Tree-in-Bud (TIB) patterns in chest CT scans.
  • To correlate quantified TIB volume with visual scoring by radiologists for disease severity assessment.
  • To integrate quantification with existing CAD systems for enhanced TIB detection.

Main Methods:

  • A novel quantification method based on a local scale concept was developed.
  • TIB regions identified by a CAD system were adaptively quantified.
  • Volume percentages were compared against radiologist visual scoring.

Main Results:

  • The study utilized 94 chest CT scans (39 HPIV, 34 NTM, 21 controls).
  • The proposed quantification system demonstrated strong agreement with radiologist assessments.
  • Observer-CAD agreement showed high correlation: R² = 0.824 for HPIV and R² = 0.801 for NTM.

Conclusions:

  • The automated TIB quantification method is effective and well-suited for integration with CAD systems.
  • This approach provides a reliable, objective measure of affected lung volume for respiratory infections.
  • The findings support the use of automated TIB quantification for improved disease severity assessment in clinical practice.