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 patients, chest computed tomography (CT) is most widely used imaging modality. An automatic method to characterize lungs patients based on individually optimized Hounsfield unit (HU) thresholds was developed implemented. Lungs were considered as composed three components—aerated, intermediate, consolidated. Three methods analytic fit (Gaussian) maximum gradient search (using polynomial original data fits) applied a population 166 scanned during first wave pandemic. Preliminarily, impact inter-scanner variability HU-density calibration curve investigated. Results showed that negligible. median values individual th1 (between aerated intermediate components) −768, −780, −798 HU for methods, respectively. A significantly lower value th2 consolidated found (−34 HU) compared other two (−114 −87 HU). quantify components our population—the aerated, consolidation 793 ± 499 cc, 914 291 126 111 respectively, while peak −853 56 HU.