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Updated: Mar 13, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Precise Lung Density Quantification with a Physics-based CT Harmonizer.

Saman Sotoudeh-Paima^1,2, David A Lynch³, Stephen M Humphries³

¹Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, 2424 Erwin Rd, Hock Plaza, Ste 302, Durham, NC 27705.

Radiology. Cardiothoracic Imaging

|March 12, 2026

Summary

This summary is machine-generated.

A new physics-based method harmonizes CT images, improving lung density measurement reproducibility. This technique enhances accuracy for conditions like Chronic Obstructive Pulmonary Disease (COPD).

Keywords:

CT Chronic Obstructive Pulmonary Disease Lung Physics Thorax

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Area of Science:

Medical Imaging
Radiology
Pulmonary Medicine

Background:

Chest CT scans exhibit variability in noise, spatial resolution, and lung volume.
These variations impact the reproducibility of lung density measurements, crucial for diagnosing and monitoring diseases like Chronic Obstructive Pulmonary Disease (COPD).

Purpose of the Study:

To develop and evaluate a physics-based image harmonization method for CT scans.
The goal is to standardize images to a reference quality index, improving lung density measurement reproducibility.

Main Methods:

A retrospective analysis of chest CT data from the COPDGene study was performed.
A harmonization algorithm adjusted spatial resolution, noise, and lung volume sequentially.
Lung density metrics, including LAA-950 and Perc15, were calculated and compared to existing methods (VALD, MF-VALD).

Main Results:

The harmonization technique significantly improved Perc15 reproducibility by 4.8-fold.
It outperformed existing methods, achieving better reproducibility coefficients on both full-dose and reduced-dose scans.
The harmonizer reduced Perc15 variability from 35.6 HU to 7.4 HU.

Conclusions:

The developed physics-based technique effectively harmonizes CT images to a reference quality index.
This harmonization improves the reproducibility of lung density metrics, aiding in more reliable disease assessment.
The method offers a significant advancement for quantitative CT analysis in pulmonary research and clinical practice.