Ajay Sheshadri1, Alfonso Rodriguez, Ryan Chen, James Kozlowski, Dana Burgdorf, Tammy Koch, Jaime Tarsi, Rebecca Schutz, Brad Wilson, Kenneth Schechtman, Joseph K Leader, Eric A Hoffman, Mario Castro, Sean B Fain, David S Gierada. 1. From the *Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, St Louis, MI; †Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; ‡Department of Surgery, University of Louisville School of Medicine, Louisville, KY; §Division of Biostatistics, Washington University School of Medicine, St Louis, MI; ∥Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA; ¶Department of Radiology, University of Iowa College of Medicine, Iowa City, IA; and #Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MI.
Abstract
OBJECTIVE: We reduced the computed tomography (CT)-reconstructed field of view (FOV), increasing pixel density across airway structures and reducing partial volume effects, to determine whether this would improve accuracy of airway wall thickness quantification. METHODS: We performed CT imaging on a lung phantom and 29 participants. Images were reconstructed at 30-, 15-, and 10-cm FOV using a medium-smooth kernel. Cross-sectional airway dimensions were compared at each FOV with repeated-measures analysis of variance. RESULTS: Phantom measurements were more accurate when FOV decreased from 30 to 15 cm (P < 0.05). Decreasing FOV further to 10 cm did not significantly improve accuracy. Human airway measurements similarly decreased by decreasing FOV (P < 0.001). Percent changes in all measurements when reducing FOV from 30 to 15 cm were less than 3%. CONCLUSIONS: Airway measurements at 30-cm FOV are near the limits of CT resolution using a medium-smooth kernel. Reducing reconstructed FOV would minimally increase sensitivity to detect differences in airway dimensions.
OBJECTIVE: We reduced the computed tomography (CT)-reconstructed field of view (FOV), increasing pixel density across airway structures and reducing partial volume effects, to determine whether this would improve accuracy of airway wall thickness quantification. METHODS: We performed CT imaging on a lung phantom and 29 participants. Images were reconstructed at 30-, 15-, and 10-cm FOV using a medium-smooth kernel. Cross-sectional airway dimensions were compared at each FOV with repeated-measures analysis of variance. RESULTS: Phantom measurements were more accurate when FOV decreased from 30 to 15 cm (P < 0.05). Decreasing FOV further to 10 cm did not significantly improve accuracy. Human airway measurements similarly decreased by decreasing FOV (P < 0.001). Percent changes in all measurements when reducing FOV from 30 to 15 cm were less than 3%. CONCLUSIONS: Airway measurements at 30-cm FOV are near the limits of CT resolution using a medium-smooth kernel. Reducing reconstructed FOV would minimally increase sensitivity to detect differences in airway dimensions.
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