Development and application of a virtual imaging trial framework for longitudinal quantification of emphysema in CT

Saman Sotoudeh-Paima; Fong Chi Ho; Mobina Ghojogh Nejad; Amar Kavuri; Bryan O'Sullivan-Murphy; David A. Lynch; W. Paul Segars; Ehsan Samei; Ehsan Abadi

doi:10.1117/12.3006925

1 April 2024 Development and application of a virtual imaging trial framework for longitudinal quantification of emphysema in CT

Saman Sotoudeh-Paima, Fong Chi Ho, Mobina Ghojogh Nejad, Amar Kavuri, Bryan O'Sullivan-Murphy, David A. Lynch, W. Paul Segars, Ehsan Samei, Ehsan Abadi

Author Affiliations +

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129251H (2024) https://doi.org/10.1117/12.3006925
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Pulmonary emphysema is a progressive lung disease that requires accurate evaluation for optimal management. This task, possible using quantitative CT, is particularly challenging as scanner and patient attributes change over time, negatively impacting the CT-derived quantitative measures. Efforts to minimize such variations have been limited by the absence of ground truth in clinical data, thus necessitating reliance on clinical surrogates, which may not have one-to-one correspondence to CT-based findings. This study aimed to develop the first suite of human models with emphysema at multiple time points, enabling longitudinal assessment of disease progression with access to ground truth. A total of 14 virtual subjects were modeled across three time points. Each human model was virtually imaged using a validated imaging simulator (DukeSim), modeling an energy-integrating CT scanner. The models were scanned at two dose levels and reconstructed with two reconstruction kernels, slice thicknesses, and pixel sizes. The developed longitudinal models were further utilized to demonstrate utility in algorithm testing and development. Two previously developed image processing algorithms (CT-HARMONICA, EmphysemaSeg) were evaluated. The results demonstrated the efficacy of both algorithms in improving the accuracy and precision of longitudinal quantifications, from 6.1±6.3% to 1.1±1.1% and 1.6±2.2% across years 0 to 5. Further investigation in EmphysemaSeg identified that baseline emphysema severity, defined as >5% emphysema at year 0, contributed to its reduced performance. This finding highlights the value of virtual imaging trials in enhancing the explainability of algorithms. Overall, the developed longitudinal human models enabled ground-truth based assessment of image processing algorithms for lung quantifications.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Saman Sotoudeh-Paima, Fong Chi Ho, Mobina Ghojogh Nejad, Amar Kavuri, Bryan O'Sullivan-Murphy, David A. Lynch, W. Paul Segars, Ehsan Samei, and Ehsan Abadi "Development and application of a virtual imaging trial framework for longitudinal quantification of emphysema in CT", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129251H (1 April 2024); https://doi.org/10.1117/12.3006925

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