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Generation of a Chronic Obstructive Pulmonary Disease Model in Mice by Repeated Ozone Exposure

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Computed Tomography Image Matching in Chronic Obstructive Pulmonary Disease.

Sandeep Bodduluri¹, Surya P Bhatt¹, Joseph M Reinhardt²

¹Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama, Birmingham, AL 35294; University of Alabama at Birmingham Lung Imaging Core, University of Alabama, Birmingham, AL 35294; University of Alabama at Birmingham Lung Health Center, University of Alabama at Birmingham, AL 35294.

Critical Reviews in Biomedical Engineering

|February 13, 2018

Summary

Computed tomography (CT) image matching offers advanced insights into chronic obstructive pulmonary disease (COPD) by linking lung structure and function. This technique provides clinically meaningful data beyond traditional spirometry and single-volume CT scans for COPD phenotyping.

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Home-Based Prescribed Pulmonary Exercise in Patients with Stable Chronic Obstructive Pulmonary Disease

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Published on: August 24, 2019

Area of Science:

Pulmonary Medicine
Radiology
Medical Imaging

Background:

Chronic obstructive pulmonary disease (COPD) involves progressive airflow obstruction with significant clinical heterogeneity.
Current diagnostic tools like spirometry lack the ability to differentiate between emphysema and small airways disease.
Computed tomography (CT) aids in disease localization but often fails to connect structural and functional lung abnormalities.

Purpose of the Study:

To review recent advancements in CT image matching for COPD phenotyping.
To highlight the clinical utility of CT image matching beyond conventional methods.
To demonstrate how CT image matching integrates structural and functional lung information.

Main Methods:

Review of recent applications of CT image matching techniques.
Analysis of CT scans from both inspiratory and expiratory phases.
Extraction of functional information such as regional ventilation and lung mechanics.

Main Results:

CT image matching provides a more comprehensive understanding of COPD by correlating structural changes with functional deficits.
This method allows for the separation of emphysema from small airways disease, offering detailed phenotyping.
Recent applications yield clinically relevant data that surpasses the limitations of spirometry and single-volume CT.

Conclusions:

CT image matching represents a significant advancement in COPD assessment.
It offers a powerful tool for detailed phenotyping and understanding disease mechanisms.
This technique enhances diagnostic capabilities and clinical stratification for COPD patients.