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

TL;DR

This paper introduces a patient-independent Gaussian-based model to quantify well-aerated lung volume in CT images, providing robust, reproducible metrics for assessing lung health and disease severity, especially in COVID-19 patients.

Contribution

It presents a novel Gaussian fit approach for lung CT analysis that is independent of reconstruction parameters and respiratory motion, enabling consistent and personalized assessment.

Findings

WAVE.f is independent of respiratory motion in 80% of cases.

Healthy and COVID-19 lungs show significant differences in metrics.

The local biomarker effectively quantifies disease severity spatially.

Abstract

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.