Two Center for Virtual Imaging Trials researchers will present their scientific works at the 2023 International Conference of the American Thoracic Society (ATS) that will be held in Washington, DC 19-24 May. Amar Kavuri and Saman Sotoudeh-Paima will present their studies concerning the application of virtual imaging techniques for emphysema quantification and score in chronic obstructive pulmonary disease (COPD). Every year, the ATS International Conference registers the participation of nearly 14,000 pulmonary, critical care, and sleep professionals from around the word.
The application to pulmonary diseases is one of the many potential translational opportunities that virtual imaging techniques offer in the medical field. In particular, Saman Sotoudeh-Paima study exploits virtual imaging datasets to inform artificial intelligence algorithms assessing emphysema score for COPD quantifications in CT. Amar Kavuri study won the ATS 2023 abstract scholarship and applies virtual imaging tools to assess the effects of intra-patient end-inspiration variability in emphysema quantification. Following are the details of the presentations.
A. Kavuri, M. Nejad, S. Sotoudeh-Paima, H. P. McAdams, D. A. Lynch, P. W. Segars, E.Samei, E. Abadi; Effects of Intra-patient End-inspiration Variability in Emphysema Quantification: A Virtual Imaging Study. American Thoracic Society, 2023. https://www.atsjournals.org/doi/pdf/10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A4022
The purpose of this study was to quantify the effects of lung respiration levels on emphysema quantification using a validated virtual imaging platform and to demonstrate the effectiveness of the current adjustment methods. Our results show 15% increased by 46 HU (95% CI:[36.4,56]) per 1L respiration volume deviation and LAA-950 reduced by 1.27% /1L (95% CI:[0.067,2.5] ) deviation. The physiologic based adjustment model reduced this variability more compared to the statistical one due to its basis on the individual patient and not population averages. This analysis helps in improving the lung volume correction methods and in reducing this variability for accurate estimation of the quantitative metrics.
Sotoudeh-Paima, S., Nejad, M. G., Segars, W. P., O’Sullivan-Murphy, B., Macintyre, N. R., Lynch, D. A., Samei E., Abadi, E. Emphysema Score in CT for COPD Quantifications Using Artificial Intelligence Informed by Virtual Imaging Datasets. American Thoracic Society, 2023. https://doi.org/10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A4021
Computed tomography (CT) is an in vivo diagnostic method that assesses the severity and extent of emphysema in the lungs. LAA-950 is a conventional imaging biomarker to quantify emphysema. While this biomarker has shown promising values in assessing disease severity, it is highly prone to variability in imaging protocols and scanner makes and models. The purpose of this work was to investigate a deep learning (DL)-based quantification approach that can characterize emphysema accurately while being robust to scanner variability. This approach would enable a more reliable comparison of emphysema quantifications in multi-center and longitudinal studies.
