A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients

PurposeQuantitative metrics in lung computed tomography (CT) images have been widely used, often without a clear connection with physiology. This work proposes patient-independent model for the estimation of well-aerated volume lungs CT (WAVE).MethodsA Gaussian fit, mean (Mu.f) and width (Sigma.f) values, was applied to lower histogram data points provide (WAVE.f). Independence from reconstruction parameters respiratory cycle analysed using healthy 4DCT acquisitions. The first order radiomic features calculated third cohort COVID-19 patients were compared those relative lungs. Each further segmented 24 subregions new biomarker derived fit parameter Mu.f proposed represent local density changes.ResultsWAVE.f resulted independent motion 80% cases. Differences 1%, 2% up 14% comparing moderate iterative strength FBP algorithm, 1 3 mm slice thickness different kernel. Healthy subjects significantly all calculated. Graphical representation provides spatial quantitative information single 2D picture.ConclusionsUnlike other based on fixed thresholds, this is able consider inter- intra-subject variability. In addition, it defines quantify severity disease, independently observer.