pubmed.ncbi.nlm.nih.gov

Quantification of Pulmonary Inflammatory Processes Using Chest Radiography: Tuberculosis as the Motivating Application - PubMed

Clinical Trial

Quantification of Pulmonary Inflammatory Processes Using Chest Radiography: Tuberculosis as the Motivating Application

Guilherme Giacomini et al. Medicine (Baltimore). 2015 Jul.

Abstract

The purpose of this work was to develop a quantitative method for evaluating the pulmonary inflammatory process (PIP) through the computational analysis of chest radiography exams in posteroanterior (PA) and lateral views. The quantification procedure was applied to patients with tuberculosis (TB) as the motivating application.A study of high-resolution computed tomography (HRCT) examinations of patients with TB was developed to establish a relation between the inflammatory process and the signal difference-to-noise ratio (SDNR) measured in the PA projection. A phantom essay was used to validate this relation, which was implemented using an algorithm that is able to estimate the volume of the inflammatory region based solely on SDNR values in the chest radiographs of patients.The PIP volumes that were quantified for 30 patients with TB were used for comparisons with direct HRCT analysis for the same patient. The Bland-Altman statistical analyses showed no significant differences between the 2 quantification methods. The linear regression line had a correlation coefficient of R = 0.97 and P < 0.001, showing a strong association between the volume that was determined by our evaluation method and the results obtained by direct HRCT scan analysis.Since the diagnosis and follow-up of patients with TB is commonly performed using X-rays exams, the method developed herein can be considered an adequate tool for quantifying the PIP with a lower patient radiation dose and lower institutional cost. Although we used patients with TB for the application of the method, this method may be used for other pulmonary diseases characterized by a PIP.

PubMed Disclaimer

Conflict of interest statement

The authors have no funding or conflicts of interest to disclose.

Figures

FIGURE 1
FIGURE 1

Manual lung segmentation performed by a radiologist. (A) Segmented lung in sagittal plane (lateral view). (B) Segmented lung in coronal plane (PA view). (C) Segmentation of pulmonary inflammatory process in coronal plane view of right lung.

FIGURE 2
FIGURE 2

Segmented lungs in sequentially expanded coronal (A) and sagittal (B) planes.

FIGURE 3
FIGURE 3

Structure of the chest homogeneous phantom used to simulate different volumes of pulmonary inflammatory processes and healthy lung background. The homogeneous phantom was constructed of 4 acrylic slabs (2.54 cm of thickness), an air gap (5.08 cm of thickness) and 2 aluminum plates (1.0 and 2.0 mm of thickness). All of the plates used measured 30.5 cm × 30.5 cm. The PIP volumes were simulated by 10 steps of polyvinyl chloride placed in the center of the phantom.

FIGURE 4
FIGURE 4

Relation between MFLIP and SDNR extracted from the analysis of 15 patient examinations, correlating measurements in the HRCT scan and PA radiographic exams of the patients, respectively. The analyzed points were adjusted with an exponential curve (solid line), with R2 = 0.80. The 2 dashed curves display the limits of 2 SD around the adjusted curve.

FIGURE 5
FIGURE 5

MFLIP versus SDNR behavior for the chest homogeneous phantom essays for 3 different radiographic techniques currently used in the clinical routine (dashed lines). The dashed lines reflect an exponential function adjustment (R2 = ∼0.98) of SDNR for each normalized steps. The solid line represents the relation obtained from patient examinations (Equation 2).

FIGURE 6
FIGURE 6

Volume quantification agreement of the proposed method (chest radiograph), compared with reference standard (HRCT) for 30 patients with TB. (A) Linear regression line were determined by y = 1.02x + 0.03, with R2 = 0.97, demonstrating low dispersion between data. (B) Bland–Altman plot for both quantification methods. The difference refers to the reference standard minus the developed method. The central line corresponds to the mean value of deviations. The dashed lines indicate the interval of 2 SD, indicating an adequate level of statistical agreement.

FIGURE 7
FIGURE 7

Reconstruction of 3D shaded surface of the lungs and pulmonary inflammatory process based on HRCT (A) visually compared with the presented quantification method using chest radiographs (B) for the same patient.

Similar articles

Cited by

References

    1. Baddeley A, Dean A, Dias HM, et al. Global Tuberculosis Report 2014. Geneva: World Health Organization; 2014.
    1. Baddeley A, Dean A, Dias HM, et al. Global Tuberculosis Report 2013. Geneva: World Health Organization; 2013.
    1. Xu Z, Bagci U, Kubler A, et al. Computer-aided detection and quantification of cavitary tuberculosis from CT scans. Med Phys 2013; 40:113701. - PMC - PubMed
    1. Zavaletta VA, Bartholmai BJ, Robb RA. High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis. Acad Radiol 2007; 14:772–787. - PMC - PubMed
    1. Huang H, Lu PX. Paving-stone CT finding in a pulmonary tuberculosis patient. Quant Imaging Med Surg 2013; 3:282–283. - PMC - PubMed

Publication types

MeSH terms