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Open Access 01-02-2016 | Chest

Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification

Authors: Leticia Gallardo-Estrella, David A. Lynch, Mathias Prokop, Douglas Stinson, Jordan Zach, Philip F. Judy, Bram van Ginneken, Eva M. van Rikxoort

Published in: European Radiology | Issue 2/2016

Abstract

Objectives

To propose and evaluate a method to reduce variability in emphysema quantification among different computed tomography (CT) reconstructions by normalizing CT data reconstructed with varying kernels.

Methods

We included 369 subjects from the COPDGene study. For each subject, spirometry and a chest CT reconstructed with two kernels were obtained using two different scanners. Normalization was performed by frequency band decomposition with hierarchical unsharp masking to standardize the energy in each band to a reference value. Emphysema scores (ES), the percentage of lung voxels below -950 HU, were computed before and after normalization. Bland-Altman analysis and correlation between ES and spirometry before and after normalization were compared. Two mixed cohorts, containing data from all scanners and kernels, were created to simulate heterogeneous acquisition parameters.

Results

The average difference in ES between kernels decreased for the scans obtained with both scanners after normalization (7.7 ± 2.7 to 0.3 ± 0.7; 7.2 ± 3.8 to -0.1 ± 0.5). Correlation coefficients between ES and FEV₁, and FEV₁/FVC increased significantly for the mixed cohorts.

Conclusions

Normalization of chest CT data reduces variation in emphysema quantification due to reconstruction filters and improves correlation between ES and spirometry.

Key Points

• Emphysema quantification is sensitive to the reconstruction kernel used.

• Normalization allows comparison of emphysema quantification from images reconstructed with varying kernels.

• Normalization allows comparison of emphysema quantification obtained with scanners from different manufacturers.

• Normalization improves correlation of emphysema quantification with spirometry.

• Normalization can be used to compare data from different studies and centers.

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Rabe KF, Hurd S, Anzueto A et al (2007) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: Gold executive summary. Am J Respir Crit Care Med 176:532–555PubMedCrossRef

Hoffman EA, Simon BA, McLennan G (2006) State of the art. A structural and functional assessment of the lung via multidetector-row computed tomography: phenotyping chronic obstructive pulmonary disease. Proc Am Thorac Soc 3:519–532PubMedPubMedCentralCrossRef

Müller NL, Staples CA, Miller RR, Abboud RT (1988) Density mask. An objective method to quantitate emphysema using computed tomography. Chest 94:782–787PubMedCrossRef

Bankier AA, De Maertelaer V, Keyzer C, Gevenois PA (1999) Pulmonary emphysema: subjective visual grading versus objective quantification with macroscopic morphometry and thin-section CT densitometry. Radiology 211:851–858PubMedCrossRef

Stolk J, Stoel BC (2011) Lung densitometry to assess progression of emphysema in chronic obstructive pulmonary disease: time to apply in the clinic? Am J Respir Crit Care Med 183:1578–1580PubMedCrossRef

Camiciottoli G, Bartolucci M, Maluccio NM et al (2006) Spirometrically gated high-resolution CT findings in COPD: lung attenuation vs lung function and dyspnea severity. Chest 129:558–564PubMedCrossRef

Madani A, van Muylem A, de Maertelaer V, Zanen J, Gevenois PA (2008) Pulmonary emphysema: size distribution of emphysematous spaces on multidetector CT images–comparison with macroscopic and microscopic morphometry. Radiology 248:1036–1041PubMedCrossRef

Mets OM, de Jong PA, van Ginneken B, Gietema HA, Lammers JWJ (2012) Quantitative computed tomography in COPD: Possibilities and limitations. Lung 190:133–145PubMedPubMedCentralCrossRef

Gierada DS, Bierhals AJ, Choong CK et al (2010) Effects of CT section thickness and reconstruction kernel on emphysema quantification relationship to the magnitude of the CT emphysema index. Acad Radiol 17:146–156PubMedPubMedCentralCrossRef

10.

Boedeker KL, McNitt-Gray MF, Rogers SR et al (2004) Emphysema: Effect of reconstruction algorithm on CT imaging measures. Radiology 232:295–301PubMedCrossRef

11.

Regan EA, Hokanson JE, Murphy JR et al (2010) Genetic epidemiology of COPD (COPDGene) study design. COPD 7:32–43PubMedPubMedCentralCrossRef

12.

van Rikxoort EM, de Hoop B, Viergever MA, Prokop M, van Ginneken B (2009) Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. Med Phys 36:2934–2947PubMedCrossRef

13.

Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310PubMedCrossRef

14.

Steiger JH (1980) Test for comparing elements in a correlation matrix. Psychol Bull 87:245–252CrossRef

15.

Schaller S, Wildberger JE, Raupach R, Niethammer M, Klingenbeck-Regn K, Flohr T (2003) Spatial domain filtering for fast modification of the tradeoff between image sharpness and pixel noise in computed tomography. IEEE Trans Med Imaging 22:846–853PubMedCrossRef

16.

Ohkubo M, Wada S, Kayugawa A, Matsumoto T, Murao K (2011) Image filtering as an alternative to the application of a different reconstruction kernel in CT imaging: feasibility study in lung cancer screening. Med Phys 38:3915–3923PubMedCrossRef

17.

Bartel ST, Bierhals AJ, Pilgram TK et al (2011) Equating quantitative emphysema measurements on different CT image reconstructions. Med Phys 38:4894–4902PubMedPubMedCentralCrossRef

18.

Choo JY, Goo JM, Lee CH, Park CM, Park SJ, Shim MS (2014) Quantitative analysis of emphysema and airway measurements according to iterative reconstruction algorithms: comparison of filtered back projection, adaptive statistical iterative reconstruction and model-based iterative reconstruction. Eur Radiol 24:799–806PubMedCrossRef

19.

Wan ES, Hokanson JE, Murphy JR et al (2011) Clinical and Radiographic Predictors of GOLD-Unclassified Smokers in the COPDGene Study. Am J Respir Crit Care Med 184:57–63PubMedPubMedCentralCrossRef

20.

Abe K, Katsuragawa S, Sasaki Y, Yanagisawa T (1992) A fully automated adaptive unsharp masking technique in digital chest radiograph. Invest Radiol 27:64–70PubMedCrossRef

21.

Stahl M, Aach T, Dippel S (2000) Digital radiography enhancement by nonlinear multiscale processing. Med Phys 27:56–65PubMedCrossRef

Title: Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification
Authors: Leticia Gallardo-Estrella
David A. Lynch
Mathias Prokop
Douglas Stinson
Jordan Zach
Philip F. Judy
Bram van Ginneken
Eva M. van Rikxoort
Publication date: 01-02-2016
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 2/2016
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-015-3824-y

At a glance: The STEP trials

Springer Medicine

Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification

Abstract

Objectives

Methods

Results

Conclusions

Key Points

At a glance: The STEP trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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