Oliver Weinheimer1, Benjamin A Hoff2, Aleksa B Fortuna2, Antonio Fernández-Baldera2, Philip Konietzke1, Mark O Wielpütz1, Terry E Robinson3, Craig J Galbán4. 1. Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, 69120 Heidelberg, Germany; Translational Lung Research Center, Heidelberg (TLRC), German Lung Research Center (DZL), 69120 Heidelberg, Germany. 2. Department of Radiology, University of Michigan, Ann Arbor, MI 48109. 3. Center of Excellence in Pulmonary Biology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94304. 4. Department of Radiology, University of Michigan, Ann Arbor, MI 48109. Electronic address: cgalban@med.umich.edu.
Abstract
RATIONALE AND OBJECTIVES: The aim of this study was to assess variability in quantitative air trapping (QAT) measurements derived from spatially aligned expiration CT scans. MATERIALS AND METHODS: Sixty-four paired CT examinations, from 16 school-age cystic fibrosis subjects examined at four separate time intervals, were used in this study. For each pair, visually inspected lobe segmentation maps were generated and expiration CT data were registered to the inspiration CT frame. Measurements of QAT, the percentage of voxels on the expiration CT scan below a set threshold were calculated for each lobe and whole-lung from the registered expiration CT and compared to the true values from the unregistered data. RESULTS: A mathematical model, which simulates the effect of variable regions of lung deformation on QAT values calculated from aligned to those from unaligned data, showed the potential for large bias. Assessment of experimental QAT measurements using Bland-Altman plots corroborated the model simulations, demonstrating biases greater than 5% when QAT was approximately 40% of lung volume. These biases were removed when calculating QAT from aligned expiration CT data using the determinant of the Jacobian matrix. We found, by Dice coefficient analysis, good agreement between aligned expiration and inspiration segmentation maps for the whole-lung and all but one lobe (Dice coefficient > 0.9), with only the lingula generating a value below 0.9 (mean and standard deviation of 0.85 ± 0.06). CONCLUSION: The subtle and predictable variability in corrected QAT observed in this study suggests that image registration is reliable in preserving the accuracy of the quantitative metrics.
RATIONALE AND OBJECTIVES: The aim of this study was to assess variability in quantitative air trapping (QAT) measurements derived from spatially aligned expiration CT scans. MATERIALS AND METHODS: Sixty-four paired CT examinations, from 16 school-age cystic fibrosis subjects examined at four separate time intervals, were used in this study. For each pair, visually inspected lobe segmentation maps were generated and expiration CT data were registered to the inspiration CT frame. Measurements of QAT, the percentage of voxels on the expiration CT scan below a set threshold were calculated for each lobe and whole-lung from the registered expiration CT and compared to the true values from the unregistered data. RESULTS: A mathematical model, which simulates the effect of variable regions of lung deformation on QAT values calculated from aligned to those from unaligned data, showed the potential for large bias. Assessment of experimental QAT measurements using Bland-Altman plots corroborated the model simulations, demonstrating biases greater than 5% when QAT was approximately 40% of lung volume. These biases were removed when calculating QAT from aligned expiration CT data using the determinant of the Jacobian matrix. We found, by Dice coefficient analysis, good agreement between aligned expiration and inspiration segmentation maps for the whole-lung and all but one lobe (Dice coefficient > 0.9), with only the lingula generating a value below 0.9 (mean and standard deviation of 0.85 ± 0.06). CONCLUSION: The subtle and predictable variability in corrected QAT observed in this study suggests that image registration is reliable in preserving the accuracy of the quantitative metrics.
Authors: Elizabeth A Belloli; Irina Degtiar; Xin Wang; Gregory A Yanik; Linda J Stuckey; Stijn E Verleden; Ella A Kazerooni; Brian D Ross; Susan Murray; Craig J Galbán; Vibha N Lama Journal: Am J Respir Crit Care Med Date: 2017-04-01 Impact factor: 21.405
Authors: Martine Loeve; Krista Gerbrands; Wim C Hop; Margaret Rosenfeld; Ieneke C Hartmann; Harm A Tiddens Journal: Chest Date: 2010-12-09 Impact factor: 9.410
Authors: Alan S Brody; Michael R Kosorok; Zhanhai Li; Lynn S Broderick; Jeffrey L Foster; Anita Laxova; Hari Bandla; Philip M Farrell Journal: J Thorac Imaging Date: 2006-03 Impact factor: 3.000
Authors: Wieying Kuo; Pierluigi Ciet; Harm A W M Tiddens; Wei Zhang; R Paul Guillerman; Marcel van Straten Journal: Am J Respir Crit Care Med Date: 2014-06-01 Impact factor: 21.405
Authors: Benjamin A Hoff; Esther Pompe; Stefanie Galbán; Dirkje S Postma; Jan-Willem J Lammers; Nick H T Ten Hacken; Leo Koenderman; Timothy D Johnson; Stijn E Verleden; Pim A de Jong; Firdaus A A Mohamed Hoesein; Maarten van den Berge; Brian D Ross; Craig J Galbán Journal: Sci Rep Date: 2017-06-07 Impact factor: 4.379
Authors: Patricia Leutz-Schmidt; Oliver Weinheimer; Bertram J Jobst; Julien Dinkel; Jürgen Biederer; Hans-Ulrich Kauczor; Michael U Puderbach; Mark O Wielpütz Journal: PLoS One Date: 2017-08-02 Impact factor: 3.240
Authors: Sundaresh Ram; Benjamin A Hoff; Alexander J Bell; Stefanie Galban; Aleksa B Fortuna; Oliver Weinheimer; Mark O Wielpütz; Terry E Robinson; Beverley Newman; Dharshan Vummidi; Aamer Chughtai; Ella A Kazerooni; Timothy D Johnson; MeiLan K Han; Charles R Hatt; Craig J Galban Journal: PLoS One Date: 2021-03-24 Impact factor: 3.752