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Computed Tomography

Computed Tomography

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

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Quantifying normal geometric variation in human pulmonary lobar geometry from high resolution computed tomography.

Ho-Fung Chan, Alys R Clark, Eric A Hoffman

Journal of Biomechanical Engineering

|March 3, 2015

Summary

This study quantifies normal human lung lobe shape using principal component analysis (PCA) on CT scans. It reveals key variations in lobe size and fissure angles, aiding in distinguishing normal anatomy from disease.

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Area of Science:

Pulmonary imaging and computational anatomy
Quantitative morphology of human lungs

Background:

Significant intersubject variability exists in ex vivo lung lobe geometry.
A quantitative understanding of normal lung shape is crucial for identifying pathological changes.

Purpose of the Study:

To quantitatively describe normal human lung lobe shape variability.
To establish a baseline for differentiating normal anatomical variations from disease-related geometric abnormalities.

Main Methods:

Principal Component Analysis (PCA) applied to high-resolution computed tomography (HRCT) imaging of 22 healthy subjects.
Finite element mesh generation of individual lung lobes to capture surface geometry.
Analysis of nine principal components explaining over 90% of shape variation per lobe.

Main Results:

Lobe size accounts for 20-50% of intersubject shape variability.
Diaphragm shape is the second most significant factor in intersubject differences.
After accounting for size, fissure angles and relative lobe size become the most significant shape differentiators.

Conclusions:

PCA effectively captures normal lung lobe shape variability.
The established quantitative framework can be used to identify and define abnormalities in lobar geometry.
This approach has potential applications in diagnosing and monitoring lung diseases.