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

Medical Physics;Machine Learning