Elsevier

Physica Medica

Volume 82, February 2021, Pages 28-39
Physica Medica

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

https://doi.org/10.1016/j.ejmp.2021.01.004Get rights and content

Highlights

  • 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.

Abstract

Purpose

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).

Methods

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.

Results

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.

Conclusions

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.

Keywords

Quantitative imaging
Computed tomography
QCT
Image analysis
Radiomic
COVID-19

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