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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Virtual Imaging Trials for Coronavirus Disease (COVID-19)
Ehsan Abadi1,2, W Paul Segars1,2,3, Hamid Chalian1
1Department of Radiology, Duke University, 2424 Erwin Rd, Ste 302, Durham, NC 27705.
Virtual imaging trials using computational models and simulators can optimize CT and radiography for COVID-19 diagnosis. This approach enhances assessment of imaging tools for better patient management during the pandemic.
Area of Science:
- Medical Imaging
- Computational Modeling
- Radiography
Background:
- Virtual imaging trials (VITs) offer a framework for assessing and optimizing imaging methods.
- Adapting VITs for coronavirus disease (COVID-19) is crucial for improving diagnostic accuracy and patient management.
Purpose of the Study:
- To demonstrate the adaptation of VITs for COVID-19 imaging studies.
- To enable effective assessment and optimization of CT and radiography techniques for COVID-19.
Main Methods:
- Developed the first computational patient models for COVID-19 using the 4D extended cardiac-torso (XCAT) model.
- Incorporated COVID-19 abnormalities segmented from clinical CT images into XCAT models.
- Utilized scanner-specific CT and radiography simulators to generate virtual images of COVID-19 phantoms.
Main Results:
- Simulated COVID-19 abnormalities appeared realistic in shape and texture.
- Demonstrated varying contrast-to-noise ratios in abnormal regions across different imaging parameters (mAs).
Conclusions:
- The developed toolsets provide a foundation for utilizing VITs in COVID-19 imaging research.
- VITs can facilitate the optimization of CT and radiography acquisitions and analysis tools for managing COVID-19.
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