Reproducibility Analysis of 4DCT Derived Ventilation Distribution Data - An Application of a Ventilation Calculation Algorithm based on 4DCT

Geoffrey G. Zhang, Kujtim Latifi, Vladimir Feygelman, Thomas J. Dilling, Eduardo G. Moros

2016

Abstract

Deriving lung ventilation distribution from 4-dimensional CT (4DCT) using deformable image registration (DIR) is a recent technical development. In this study, we evaluated the serial reproducibility of ventilation data derived from two separate 4DCT data sets, collected at different time points. A total of 33 lung cancer patients were retrospectively analyzed. All patients had two stereotactic body radiotherapy treatment courses for lung cancer. Seven patients were excluded due to artifacts in the 4DCT data sets. The ventilation distributions in the lungs for each patient were calculated using the two sets of planning 4DCT data. The deformation matrices between the expiration and inspiration phases generated by DIR were used to produce ventilation distributions using the ΔV method. Ventilation in the lung regions that received less than 1 Gy was analyzed. For the 26 cases, the median Spearman correlation coefficient value was 0.31 (range 0.18 to 0.52, p value < 0.01 for all cases). The median Dice similarity coefficient value between the upper 30% ventilation regions of the two sets was 0.75 (range 0.71 to 0.81, Figure 1). We conclude that the two ventilation data sets in each case correlated and the reproducibility over time was reasonably good.

References

  1. Castillo, R., Castillo, E., Mccurdy, M., Gomez, D. R., Block, A. M., Bergsma, D., Joy, S. & Guerrero, T. 2012. Spatial Correspondence of 4d Ct Ventilation and Spect Pulmonary Perfusion Defects in Patients with Malignant Airway Stenosis. Physics in Medicine and Biology, 57, 1855-1871.
  2. Dice, L. R. 1945. Measures of the Amount of Ecologic Association between Species. Ecology, 26, 297-302.
  3. Ding, K., Bayouth, J. E., Buatti, J. M., Christensen, G. E. & Reinhardt, J. M. 2010. 4dct-Based Measurement of Changes in Pulmonary Function Following a Course of Radiation Therapy. Med Phys, 37, 1261-1272.
  4. Ding, K., Cao, K., Fuld, M. K., Du, K., Christensen, G. E., Hoffman, E. A. & Reinhardt, J. M. 2012. Comparison of Image Registration Based Measures of Regional Lung Ventilation from Dynamic Spiral CT with XeCt. Med Phys, 39, 5084-5098.
  5. Guerrero, T., Sanders, K., Noyola-Martinez, J., Castillo, E., Zhang, Y., Tapia, R., Guerra, R., Borghero, Y. & Komaki, R. 2005. Quantification of Regional Ventilation from Treatment Planning CT. Int J Radiat Oncol Biol Phys, 62, 630-634.
  6. Harris, B., Bailey, D., Miles, S., Bailey, E., Rogers, K., Roach, P., Thomas, P., Hensley, M. & King, G. G. 2007. Objective Analysis of Tomographic Ventilation-Perfusion Scintigraphy in Pulmonary Embolism. Am J Resp Crit Care Med, 175, 1173- 1180.
  7. Huang, T.-C., Hsiao, C.-Y., Chien, C.-R., Liang, J.-A., Shih, T.-C. & Zhang, G. 2013. IMRT Treatment Plans and Functional Planning with Functional Lung Imaging From 4d-Ct for Thoracic Cancer Patients. Radiat Oncol, 8, 3.
  8. Janssens, G., De Xivry, J. O., Fekkes, S., Dekker, A., Macq, B., Lambin, P. & Van Elmpt, W. 2009. Evaluation of Nonrigid Registration Models for Interfraction Dose Accumulation in Radiotherapy. Med Phys, 36, 4268-4276.
  9. Kipritidis, J., Siva, S., Hofman, M. S., Callahan, J., Hicks, R. J. & Keall, P. J. 2014. Validating and Improving Ct Ventilation Imaging By Correlating With Ventilation 4d-Pet/Ct Using 68ga-Labeled Nanoparticles. Med Phys, 41, 011910.
  10. Latifi, K., Dilling, T., Feygelman, V., Moros, E., Stevens, C., Montilla-Soler, J. & Zhang, G. 2015. Impact Of Dose On Lung Ventilation Change Calculated From 4d-Ct Using Deformable Image Registration In Lung Cancer Patients Treated With Sbrt. Journal Of Radiation Oncology, 4, 265-270.
  11. Latifi, K., Feygelman, V., Moros, E. G., Dilling, T. J., Stevens, C. W. & Zhang, G. G. 2013a. Normalization of Ventilation Data from 4d-Ct to Facilitate Comparison Between Datasets Acquired at Different Times. Plos One, 8, E84083.
  12. Latifi, K., Huang, T.-C., Feygelman, V., Budzevich, M. M., Moros, E. G., Dilling, T. J., Stevens, C. W., Elmpt, W. V., Dekker, A. & Zhang, G. G. 2013b. Effects of Quantum Noise in 4d-Ct on Deformable Image Registration and Derived Ventilation Data. Phys Med Biol, 58, 7661-7672.
  13. Latifi, K., Zhang, G., Stawicki, M., Van Elmpt, W., Dekker, A. & Forster, K. 2013c. Validation of Three Deformable Image Registration Algorithms for the Thorax. J Appl Clin Med Phys, 14, 19-30.
  14. Melo, M. F. V., Layfield, D., Harris, R. S., O'neill, K., Musch, G., Richter, T., Winkler, T., Fischman, A. J. & Venegas, J. G. 2003. Quantification of Regional Ventilation-Perfusion Ratios with Pet. J Nucl Med, 44, 1982-1991.
  15. Reinhardt, J. M., Ding, K., Cao, K., Christensen, G. E., Hoffman, E. A. & Bodas, S. V. 2008. RegistrationBased Estimates of Local Lung Tissue Expansion Compared to Xenon CT Measures of Specific Ventilation. Medical Image Analysis, 12, 752-763.
  16. Simon, B. A. 2000. Non-Invasive Imaging of Regional Lung Function using X-Ray Computed Tomography. J Clin Monitoring Computing, 16, 433-442.
  17. Siva, S., Thomas, R., Callahan, J., Hardcastle, N., Pham, D., Kron, T., Hicks, R. J., Macmanus, M. P., Ball, D. L. & Hofman, M. S. 2015. High-Resolution Pulmonary Ventilation and Perfusion PET/CT Allows for Functionally Adapted Intensity Modulated Radiotherapy In Lung Cancer. Radiotherapy and Oncology, 115, 157-162.
  18. Yamamoto, T., Kabus, S., Lorenz, C., Mittra, E., Hong, J. C., Chung, M., Eclov, N., To, J., Diehn, M., Loo Jr, B. W. & Keall, P. J. 2014. Pulmonary Ventilation Imaging Based On 4-Dimensional Computed Tomography: Comparison With Pulmonary Function Tests And Spect Ventilation Images. Int J Radiat Oncol Biol Phys.
  19. Zhang, G., Huang, T.-C., Dilling, T., Stevens, C. & Forster, K. 2011. Comments on 'Ventilation from Four-dimensional Computed Tomography: Density versus Jacobian Methods'. Phys Med Biol, 56, 3445- 3446.
  20. Zhang, G. G., Huang, T. C., Dilling, T., Stevens, C. & Forster, K. M. 2009. Derivation of High-Resolution Pulmonary Ventilation Using Local Volume Change in Four-dimensional CT Data. In: Dössel, O. & Schlegel, W. C. (Eds.) World Congress on Medical Physics and Biomedical Engineering. Munich, Germany: Springer.
Download


Paper Citation


in Harvard Style

Zhang G., Latifi K., Feygelman V., Dilling T. and Moros E. (2016). Reproducibility Analysis of 4DCT Derived Ventilation Distribution Data - An Application of a Ventilation Calculation Algorithm based on 4DCT . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 40-43. DOI: 10.5220/0005747400400043


in Bibtex Style

@conference{bioimaging16,
author={Geoffrey G. Zhang and Kujtim Latifi and Vladimir Feygelman and Thomas J. Dilling and Eduardo G. Moros},
title={Reproducibility Analysis of 4DCT Derived Ventilation Distribution Data - An Application of a Ventilation Calculation Algorithm based on 4DCT},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016)},
year={2016},
pages={40-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005747400400043},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016)
TI - Reproducibility Analysis of 4DCT Derived Ventilation Distribution Data - An Application of a Ventilation Calculation Algorithm based on 4DCT
SN - 978-989-758-170-0
AU - Zhang G.
AU - Latifi K.
AU - Feygelman V.
AU - Dilling T.
AU - Moros E.
PY - 2016
SP - 40
EP - 43
DO - 10.5220/0005747400400043