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

• Published in: Physica medica Vol. 82; pp. 28 - 39
• Main Authors: Berta, L., De Mattia, C., Rizzetto, F., Carrazza, S., Colombo, P.E., Fumagalli, R., Langer, T., Lizio, D., Vanzulli, A., Torresin, A.
• Format: Journal Article
• Language: English
• Published: Italy Elsevier Ltd 01.02.2021; Associazione Italiana di Fisica Medica. Published by Elsevier Ltd
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Computed tomography; COVID-19; COVID-19 - diagnostic imaging; Female; Humans; Image analysis; Image Processing, Computer-Assisted; Lung - diagnostic imaging; Lung Diseases - diagnostic imaging; Male; Middle Aged; Original Paper; QCT; Quantitative imaging; Radiomic; Tomography, X-Ray Computed; Young Adult; COVID-19; Radiomic; Image analysis; Quantitative imaging; QCT; Computed tomography
• DOI: 10.1016/j.ejmp.2021.01.004
• ISSN: 1120-1797; 1724-191X

•A new approach has been proposed to overcome limitations of quantitative metrics calculated from lung CT images.•New metrics, derived from physics assumptions and with physiological significance, are introduced.•New metrics show lower dependencies from CT-number and reduced inter and intra patient variability.•Quantitative metrics of lung COVID-19 diseases can be described by subdividing the organ into 24 sub-regions. 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.