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10.1117/1.JMI.8.S1.013501 http://scihub22266oqcxt.onion/10.1117/1.JMI.8.S1.013501 33447646!7791575!33447646     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Imaging+(Bellingham) 2021 ; 8 (Suppl 1): 013501 Nephropedia Template TP {{tp|p=33447646|t=2021. Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials |pdf=|usr=}}{{33447646}} gab.com Text Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials JO: J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33447646 #FOAvip 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 task-specific 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 x 0.5 x 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. Twit Text FOAVip Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials ????J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33447646 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33447646 #FOAvip English Wikipedia <ref>{{Cite pmid|33447646}}</ref> Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials #MMPMID33447646 Rodriguez Perez S; Coolen J; Marshall NW; Cockmartin L; Biebau C; Desmet J; De Wever W; Struelens L; Bosmans H J Med Imaging (Bellingham) 2021[Jan]; 8 (Suppl 1): 013501 PMID33447646show ga 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 task-specific 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 x 0.5 x 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. äMethodology to create 3D models of COVID-19 pathologies for virtual cl(epmc).pdf DeepDyve Pubget Overpricing