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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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Updated: May 15, 2025

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

Optimal Window Settings for Detection and Characterization of Ground-Glass Opacities on Computed Tomography in

Marron Daud¹, S Nahum Goldberg¹, Dotan Cohen¹

¹Department of Radiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.

The Israel Medical Association Journal : IMAJ

|May 14, 2025

Summary

This summary is machine-generated.

Optimal CT window settings improve COVID-19 diagnosis. Ground-glass opacity (GGO) detection is enhanced with specific lower window settings, while dense consolidations are best viewed with manufacturer-recommended settings.

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Last Updated: May 15, 2025

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

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Published on: December 19, 2020

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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Published on: August 28, 2018

Area of Science:

Radiology
Medical Imaging
Pulmonology

Background:

Coronavirus disease-2019 (COVID-19) diagnosis relies on chest computed tomography (CT) findings, including ground-glass opacity (GGO) and consolidations.
Accurate identification of these CT features is critical for patient management.

Purpose of the Study:

To determine optimal CT window settings for characterizing and detecting GGO and dense consolidations in COVID-19 patients.
To compare novel window settings with manufacturer recommendations using a Simplex-based approach.

Main Methods:

A Simplex algorithm was employed to iteratively optimize window settings (width and center) based on radiologist evaluations.
Radiologists graded the characterization and detection of GGO and dense consolidations across various window settings.
A two-phase study involved initial optimization and subsequent comparison of optimal versus manufacturer-recommended settings on 54 COVID-19 CT scans.

Main Results:

Optimal window settings for GGO characterization and detection were identified as 630 HU (center) and 1460 HU (width), significantly outperforming manufacturer settings (P = 0.005).
Optimal window settings for dense consolidations were similar to manufacturer recommendations (-585 HU and 1800 HU).
In a comparative analysis, 78% of readers preferred the newly determined optimal window settings over conventional ones.

Conclusions:

Specific, lower CT windowing widths than manufacturer recommendations enhance the visualization of GGO lung opacities in COVID-19.
Denser consolidations in COVID-19 patients are optimally visualized using manufacturer-recommended CT window settings.
These findings suggest distinct pathophysiological processes influencing optimal imaging parameters for different COVID-19 lung manifestations.