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About this Publication
Title
Equating quantitative emphysema measurements on different CT image reconstructions.
Pubmed ID
21928661 (View this publication on the PubMed website)
Publication
Med Phys. 2011 Aug; Volume 38 (Issue 8): Pages 4894-902
Authors

Bartel ST, Bierhals AJ, Pilgram TK, Hong C, Schechtman KB, Conradi SH, Gierada DS

Abstract

PURPOSE: To mathematically model the relationship between CT measurements of emphysema obtained from images reconstructed using different section thicknesses and kernels and to evaluate the accuracy of the models for converting measurements to those of a reference reconstruction.

METHODS: CT raw data from the lung cancer screening examinations of 138 heavy smokers were reconstructed at 15 different combinations of section thickness and kernel. An emphysema index was quantified as the percentage of the lung with attenuation below -950 HU (EI950). Linear, quadratic, and power functions were used to model the relationship between EI950 values obtained with a reference 1 mm, medium smooth kernel reconstruction and values from each of the other 14 reconstructions. Preferred models were selected using the corrected Akaike information criterion (AICc), coefficients of determination (R2), and residuals (conversion errors), and cross-validated by a jackknife approach using the leave-one-out method.

RESULTS: The preferred models were power functions, with model R2 values ranging from 0.949 to 0.998. The errors in converting EI950 measurements from other reconstructions to the 1 mm, medium smooth kernel reconstruction in leave-one-out testing were less than 3.0 index percentage points for all reconstructions, and less than 1.0 index percentage point for five reconstructions. Conversion errors were related in part to image noise, emphysema distribution, and attenuation histogram parameters. Conversion inaccuracy related to increased kernel sharpness tended to be reduced by increased section thickness.

CONCLUSIONS: Image reconstruction-related differences in quantitative emphysema measurements were successfully modeled using power functions.

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