Computed Tomography Image Matching in Chronic Obstructive Pulmonary Disease

Abstract

Chronic obstructive pulmonary disease (COPD), characterized by progressive airflow obstruction due to the combined effects of emphysema and small airways disease, is associated with high morbidity and mortality. The complex link between emphysema and airways disease is associated with significant heterogeneity in clinical presentation. Spirometry is the current gold standard for diagnosis and stratification of the severity of airflow obstruction in COPD. Although spirometry is simple to use, it does not enable the separation of emphysema from airways disease. Computed tomography (CT), on the other hand, provides the anatomic localization of disease and has been increasingly used to phenotype COPD. The majority of current CT measures are extracted from a single-volume CT scan and although useful to characterize emphysema and airways disease, they do not link structural and functional abnormalities. Alternatively, CT image matching combines information from both inspiratory and expiratory CT scans, thus enabling determination of functional changes such as regional ventilation and mechanical properties of the lung. In this review, we discuss recent applications of CT image matching that provide clinically meaningful information beyond spirometry and single-volume CT scan measures.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images

“…In small airway assessments, the inspiratory CT image is often defined as I m and expiratory image as I f . 37,38 Image matching is an optimization problem, where a transformation function is used to deform an inspiratory CT image; difference between a deformed inspiratory image and an expiratory image (cost function) is minimized in each iteration. After updating the transformation function parameters using an optimizer, the matching process is resumed to reach an accurate point-to point correspondence (Fig.…”

“…Multiple algorithms are applied for image registration. 37,38 In 2010, a pulmonary image registration platform, called the EMPIRE10 Challenge, was organized to overview different registration algorithms. 39 Generally, iterative deformable registration algorithms integrate constraints to enforce the transformation invertibility and improve the registration accuracy; however, they are timeconsuming to perform, increase computational costs, and limit the feasibility of applying these algorithms in routine clinical practice.…”

“…The second approach extracts mechanical and functional information of lung parenchyma and estimates lung volume changes by quantifying deformation between inspiration and expiration. 37,38 The widespread availability of image registration algorithms has allowed for the rapid development of new analytical QCT techniques, thereby improving AT measurements. 41 EARLY QCT METHODS Percent Gas Trapping (% Voxels <−856 HU on Expiratory CT, Low Attenuation Area [% LAA exp < −856]) Small airways cannot be visualized directly using the current radiological techniques.…”

“…1). Multiple algorithms are applied for image registration 37,38. In 2010, a pulmonary image registration platform, called the EMPIRE10 Challenge, was organized to overview different registration algorithms 39.…”

Disease activity in COPD: time to make imaging biomarkers a PET project?

“…Quantitative CT methods have generated a wealth of candidate biomarkers that probe structural alterations in the lung parenchyma, such as the lung density measurements reported in the current study [ 8 ], as well as airways and pulmonary vessels. Paired inspiratory-expiratory CT may also be analysed for indirect functional biomarkers [ 20 ], such as small airways disease or gas trapping [ 21 , 22 ], and lung biomechanics [ 23 ]. In contrast, MRI using inhaled hyperpolarised gases ( 3 He or 129 Xe) provides direct functional information in the form of pulmonary ventilation distribution and gas exchange [ 24 ] that are highly abnormal and regionally heterogeneous in patients with COPD [ 25 , 26 ].…”