Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials

Research output: Contribution to journalSpecial issuepeer-review

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Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials. / Rodriguez Perez, Sunay; Coolen, Johan; Marshall, Nicholas W.; Cockmartin, Lesley; Biebaû, Charlotte ; Desmet, Koen; De Wever, Walter; Struelens, Lara; Bosmans, Hilde.

In: Journal of Medical Imaging, Vol. 8, No. S1, 04.01.2021, p. 1-17.

Research output: Contribution to journalSpecial issuepeer-review

Harvard

Rodriguez Perez, S, Coolen, J, Marshall, NW, Cockmartin, L, Biebaû, C, Desmet, K, De Wever, W, Struelens, L & Bosmans, H 2021, 'Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials', Journal of Medical Imaging, vol. 8, no. S1, pp. 1-17. https://doi.org/10.1117/1.JMI.8.S1.013501

APA

Rodriguez Perez, S., Coolen, J., Marshall, N. W., Cockmartin, L., Biebaû, C., Desmet, K., De Wever, W., Struelens, L., & Bosmans, H. (2021). Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials. Journal of Medical Imaging, 8(S1), 1-17. https://doi.org/10.1117/1.JMI.8.S1.013501

Vancouver

Rodriguez Perez S, Coolen J, Marshall NW, Cockmartin L, Biebaû C, Desmet K et al. Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials. Journal of Medical Imaging. 2021 Jan 4;8(S1):1-17. https://doi.org/10.1117/1.JMI.8.S1.013501

Author

Rodriguez Perez, Sunay ; Coolen, Johan ; Marshall, Nicholas W. ; Cockmartin, Lesley ; Biebaû, Charlotte ; Desmet, Koen ; De Wever, Walter ; Struelens, Lara ; Bosmans, Hilde. / Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials. In: Journal of Medical Imaging. 2021 ; Vol. 8, No. S1. pp. 1-17.

Bibtex - Download

@article{39e66dd7fb9d4a70bd0e06c5389184aa,
title = "Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials",
abstract = "Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for taskspecific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.",
keywords = "COVID-19 pathologies, Voxel phantoms, Mesh modeling, COVID-19 imaging, Computed tomography segmentation, Computer simulations",
author = "{Rodriguez Perez}, Sunay and Johan Coolen and Marshall, {Nicholas W.} and Lesley Cockmartin and Charlotte Bieba{\^u} and Koen Desmet and {De Wever}, Walter and Lara Struelens and Hilde Bosmans",
note = "Score=10",
year = "2021",
month = jan,
day = "4",
doi = "10.1117/1.JMI.8.S1.013501",
language = "English",
volume = "8",
pages = "1--17",
journal = "Journal of Medical Imaging",
issn = "2329-4302",
publisher = "SPIE - Society of Photo-optical Instrumentation Engineers",
number = "S1",

}

RIS - Download

TY - JOUR

T1 - Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials

AU - Rodriguez Perez, Sunay

AU - Coolen, Johan

AU - Marshall, Nicholas W.

AU - Cockmartin, Lesley

AU - Biebaû, Charlotte

AU - Desmet, Koen

AU - De Wever, Walter

AU - Struelens, Lara

AU - Bosmans, Hilde

N1 - Score=10

PY - 2021/1/4

Y1 - 2021/1/4

N2 - Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for taskspecific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.

AB - Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for taskspecific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.

KW - COVID-19 pathologies

KW - Voxel phantoms

KW - Mesh modeling

KW - COVID-19 imaging

KW - Computed tomography segmentation

KW - Computer simulations

UR - https://ecm.sckcen.be/OTCS/llisapi.dll/open/42348628

U2 - 10.1117/1.JMI.8.S1.013501

DO - 10.1117/1.JMI.8.S1.013501

M3 - Special issue

VL - 8

SP - 1

EP - 17

JO - Journal of Medical Imaging

JF - Journal of Medical Imaging

SN - 2329-4302

IS - S1

ER -

ID: 7046353