An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
The ongoing COVID-19 pandemic currently involves millions of people worldwide. Radiology plays an important role in the diagnosis and management of patients, and chest computed tomography (CT) is the most widely used imaging modality. An automatic method to characterize the lungs of COVID-19 patient...
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doaj-a08e5facc43646a0ba2661c8b25abede2021-01-30T00:02:42ZengMDPI AGApplied Sciences2076-34172021-01-01111238123810.3390/app11031238An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 PatientsAldo Mazzilli0Claudio Fiorino1Alessandro Loria2Martina Mori3Pier Giorgio Esposito4Diego Palumbo5Francesco de Cobelli6Antonella del Vecchio7Department of Medical Physics, San Raffaele Scientific Institute, 20132 Milan, ItalyDepartment of Medical Physics, San Raffaele Scientific Institute, 20132 Milan, ItalyDepartment of Medical Physics, San Raffaele Scientific Institute, 20132 Milan, ItalyDepartment of Medical Physics, San Raffaele Scientific Institute, 20132 Milan, ItalyDepartment of Medical Physics, San Raffaele Scientific Institute, 20132 Milan, ItalyDepartment of Medicine, Università Vita-Salute San Raffaele, 20132 Milan, ItalyDepartment of Medicine, Università Vita-Salute San Raffaele, 20132 Milan, ItalyDepartment of Medical Physics, San Raffaele Scientific Institute, 20132 Milan, ItalyThe ongoing COVID-19 pandemic currently involves millions of people worldwide. Radiology plays an important role in the diagnosis and management of patients, and chest computed tomography (CT) is the most widely used imaging modality. An automatic method to characterize the lungs of COVID-19 patients based on individually optimized Hounsfield unit (HU) thresholds was developed and implemented. Lungs were considered as composed of three components—aerated, intermediate, and consolidated. Three methods based on analytic fit (Gaussian) and maximum gradient search (using polynomial and original data fits) were implemented. The methods were applied to a population of 166 patients scanned during the first wave of the pandemic. Preliminarily, the impact of the inter-scanner variability of the HU-density calibration curve was investigated. Results showed that inter-scanner variability was negligible. The median values of individual thresholds th1 (between aerated and intermediate components) were −768, −780, and −798 HU for the three methods, respectively. A significantly lower median value for th2 (between intermediate and consolidated components) was found for the maximum gradient on the data (−34 HU) compared to the other two methods (−114 and −87 HU). The maximum gradient on the data method was applied to quantify the three components in our population—the aerated, intermediate, and consolidation components showed median values of 793 ± 499 cc, 914 ± 291 cc, and 126 ± 111 cc, respectively, while the median value of the first peak was −853 ± 56 HU.https://www.mdpi.com/2076-3417/11/3/1238Covid-19chest CTlungsHU density |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aldo Mazzilli Claudio Fiorino Alessandro Loria Martina Mori Pier Giorgio Esposito Diego Palumbo Francesco de Cobelli Antonella del Vecchio |
spellingShingle |
Aldo Mazzilli Claudio Fiorino Alessandro Loria Martina Mori Pier Giorgio Esposito Diego Palumbo Francesco de Cobelli Antonella del Vecchio An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients Applied Sciences Covid-19 chest CT lungs HU density |
author_facet |
Aldo Mazzilli Claudio Fiorino Alessandro Loria Martina Mori Pier Giorgio Esposito Diego Palumbo Francesco de Cobelli Antonella del Vecchio |
author_sort |
Aldo Mazzilli |
title |
An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients |
title_short |
An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients |
title_full |
An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients |
title_fullStr |
An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients |
title_full_unstemmed |
An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients |
title_sort |
automatic approach for individual hu-based characterization of lungs in covid-19 patients |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-01-01 |
description |
The ongoing COVID-19 pandemic currently involves millions of people worldwide. Radiology plays an important role in the diagnosis and management of patients, and chest computed tomography (CT) is the most widely used imaging modality. An automatic method to characterize the lungs of COVID-19 patients based on individually optimized Hounsfield unit (HU) thresholds was developed and implemented. Lungs were considered as composed of three components—aerated, intermediate, and consolidated. Three methods based on analytic fit (Gaussian) and maximum gradient search (using polynomial and original data fits) were implemented. The methods were applied to a population of 166 patients scanned during the first wave of the pandemic. Preliminarily, the impact of the inter-scanner variability of the HU-density calibration curve was investigated. Results showed that inter-scanner variability was negligible. The median values of individual thresholds th1 (between aerated and intermediate components) were −768, −780, and −798 HU for the three methods, respectively. A significantly lower median value for th2 (between intermediate and consolidated components) was found for the maximum gradient on the data (−34 HU) compared to the other two methods (−114 and −87 HU). The maximum gradient on the data method was applied to quantify the three components in our population—the aerated, intermediate, and consolidation components showed median values of 793 ± 499 cc, 914 ± 291 cc, and 126 ± 111 cc, respectively, while the median value of the first peak was −853 ± 56 HU. |
topic |
Covid-19 chest CT lungs HU density |
url |
https://www.mdpi.com/2076-3417/11/3/1238 |
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