Literature DB >> 8946367

Density resolution in quantitative computed tomography of foam and lung.

G J Kemerink1, H H Kruize, R J Lamers, J M van Engelshoven.   

Abstract

This study was performed to assess density resolution in quantitative computed tomography (CT) of foam and lung. Density resolution, a measure for the ability to discriminate materials of different density in a CT number histogram, is normally determined by quantum noise. In a cellular solid, variations in mass in the volumes sampled by CT cause an additional degradation of density resolution by the linear partial volume effect. The sample volume, which is directly related to spatial resolution, can be varied by choosing different section thicknesses and reconstruction filters. Several polyethene (PE) foams, as simple models of lung tissue, and five patients were investigated using various sample volumes. For the uniform PE foams, density resolution could be directly determined as the full width at half maximum of CT number histograms. Density resolution for foams with cell sizes of 0.8-1.5 mm was dominated by effects caused by the limited sample size, not by quantum noise. The relative magnitudes of density resolution could roughly be explained with a model for a hypothetic random cellular solid. Since lungs are not of uniform density, analysis of patient data was more complicated. A combined convolution least-squares fit procedure, together with information obtained in the studies of foam, were used to determine density resolution in lung studies. Density resolution, both for foams and lung, was strongly dependent on sample volume, and was quite poor for thin sections and sharp filters. Consequently, histogram-shape related parameters are sensitive to the spatial resolution chosen on CT. Thin section densitometry, using a 1-mm section with a standard or high resolution filter, is not recommended except in determining average density. When using thicker sections, an in-plane spatial resolution similar to section thickness is advised.

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Year:  1996        PMID: 8946367     DOI: 10.1118/1.597757

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  15 in total

1.  Selection of patients for lung volume reduction surgery using a power law analysis of the computed tomographic scan.

Authors:  H O Coxson; K P Whittall; Y Nakano; R M Rogers; F C Sciurba; R J Keenan; J C Hogg
Journal:  Thorax       Date:  2003-06       Impact factor: 9.139

2.  Influence of CT reconstruction settings on extremely low attenuation values for specific gas volume calculation in severe emphysema.

Authors:  Caterina Salito; Jason C Woods; Andrea Aliverti
Journal:  Acad Radiol       Date:  2011-07-12       Impact factor: 3.173

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Authors:  Edoardo Cavigli; Gianna Camiciottoli; Stefano Diciotti; Ilaria Orlandi; Cheti Spinelli; Eleonora Meoni; Luca Grassi; Carmela Farfalla; Massimo Pistolesi; Fabio Falaschi; Mario Mascalchi
Journal:  Eur Radiol       Date:  2009-02-18       Impact factor: 5.315

4.  A low-cost density reference phantom for computed tomography.

Authors:  Zachary H Levine; Mingdong Li; Anthony P Reeves; David F Yankelevitz; Joseph J Chen; Eliot L Siegel; Adele Peskin; Diana N Zeiger
Journal:  Med Phys       Date:  2009-02       Impact factor: 4.071

5.  Quantitative chest tomography in COPD research: chairman's summary.

Authors:  Harvey O Coxson
Journal:  Proc Am Thorac Soc       Date:  2008-12-15

Review 6.  Anniversary paper. Development of x-ray computed tomography: the role of medical physics and AAPM from the 1970s to present.

Authors:  Xiaochuan Pan; Jeffrey Siewerdsen; Patrick J La Riviere; Willi A Kalender
Journal:  Med Phys       Date:  2008-08       Impact factor: 4.071

7.  Effect of the positron range of 18F, 68Ga and 124I on PET/CT in lung-equivalent materials.

Authors:  Gerrit J Kemerink; Mariëlle G W Visser; Renee Franssen; Emiel Beijer; Mariangela Zamburlini; Servé G E A Halders; Boudewijn Brans; Felix M Mottaghy; Gerrit J J Teule
Journal:  Eur J Nucl Med Mol Imaging       Date:  2011-02-02       Impact factor: 9.236

8.  Reconstruction algorithms influence the follow-up variability in the longitudinal CT emphysema index measurements.

Authors:  Bruno Hochhegger; Klaus Loureiro Irion; Edson Marchiori; Jose Silva Moreira
Journal:  Korean J Radiol       Date:  2011-03-03       Impact factor: 3.500

9.  Development of digital phantoms based on a finite element model to simulate low-attenuation areas in CT imaging for pulmonary emphysema quantification.

Authors:  Stefano Diciotti; Alessandro Nobis; Stefano Ciulli; Nicholas Landini; Mario Mascalchi; Nicola Sverzellati; Bernardo Innocenti
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-11-12       Impact factor: 2.924

10.  Prevalence and correlates of pulmonary emphysema in smokers and former smokers. A densitometric study of participants in the ITALUNG trial.

Authors:  Gianna Camiciottoli; Edoardo Cavigli; Luca Grassi; Stefano Diciotti; Ilaria Orlandi; Marco Zappa; Giulia Picozzi; Andrea Lopes Pegna; Eugenio Paci; Fabio Falaschi; Mario Mascalchi
Journal:  Eur Radiol       Date:  2008-08-09       Impact factor: 5.315

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