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Detecting Out-of-Phase Ventilation Using 4DCT to Improve Radiation Therapy for Lung Cancer

  • Wei ShaoEmail author
  • Taylor J. Patton
  • Sarah E. Gerard
  • Yue Pan
  • Joseph M. Reinhardt
  • John E. Bayouth
  • Oguz C. Durumeric
  • Gary E. Christensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

Functional avoidance radiation therapy (RT) uses lung function images to identify and minimize irradiation of high-function lung tissue. Lung function can be estimated by local expansion ratio (LER) of the lung, which we define in this paper as the ratio of the maximum to the minimum local lung volume in a breathing cycle. LER is computed using deformable image registration. The end exhale (0EX) and the end inhale (100IN) phases of four-dimensional computed tomography (4DCT) are often used to estimate LER, which we refer to as LER3D. However, the lung may have out-of-phase ventilation, i.e., local lung volume change is out of phase with respect to global lung expansion and contraction. We propose the LER4D measure which estimates the LER measure using all phases of 4DCT. The purpose of this paper is to quantify the amount of out-of-phase ventilation of the lung. Out-of-phase ventilation is defined to occur when the LER4D measure is \(5\%\) or more than the LER3D measure. 4DCT scans of 14 human subjects were used in this study. Low-function (high-function) regions are defined as regions that have less (greater) than \(10\%\) expansion. Our results show that on average \(19.3\%\) of the lung had out-of-phase ventilation; \(3.8\%\) of the lung had out-of-phase ventilation and is labeled as low-function by both LER3D and LER4D; \(9.6\%\) of the lung is labeled as low-function by LER3D while high-function by LER4D; and \(5.9\%\) of the lung had out-of-phase ventilation and is labeled as high-function by both LER3D and LER4D. We conclude that out-of-phase ventilation is common in all 14 human subjects we have investigated.

Keywords

Out-of-phase ventilation Ventilation imaging Radiation therapy Lung cancer 

Notes

Acknowledgments

This work is supported in part by National Cancer Institute of the National Institute of Health (NIH) under award numbers CA166703 and CA166119.

References

  1. 1.
    Cao, K., Ding, K., Christense, G.E., Reinhardt, J.M.: Tissue volume and vesselness measure preserving nonrigid registration of lung CT images. Proc. SPIE 7623, 762309 (2010)CrossRefGoogle Scholar
  2. 2.
    Cao, K., Du, K., Ding, K., Reinhardt, J., Christensen, G.: Regularized nonrigid registration of lung CT images by preserving tissue volume and vesselness measure. In: Medical Image Analysis For The Clinic: A Grand Challenge, pp. 43–54, January 2010Google Scholar
  3. 3.
    Christian, J.A., et al.: The incorporation of spect functional lung imaging into inverse radiotherapy planning for non-small cell lung cancer. Radiother. Oncol. 77(3), 271–277 (2005)CrossRefGoogle Scholar
  4. 4.
    Delaney, G., Jacob, S., Featherstone, C., Barton, M.: The role of radiotherapy in cancer treatment. Cancer 104(6), 1129–1137 (2005).  https://doi.org/10.1002/cncr.21324CrossRefGoogle Scholar
  5. 5.
    Ding, K., Bayouth, J.E., Buatti, J.M., Christensen, G.E., Reinhardt, J.M.: 4DCT-based measurement of changes in pulmonary function following a course of radiation therapy. Med. Phys. 37(3), 1261–1272 (2010)CrossRefGoogle Scholar
  6. 6.
    Ding, K., et al.: Comparison of image registration based measures of regional lung ventilation from dynamic spiral CT with Xe-CT. Med. Phys. 39(8), 5084–5098 (2012)CrossRefGoogle Scholar
  7. 7.
    Gorbunova, V., Lo, P., Ashraf, H., Dirksen, A., Nielsen, M., de Bruijne, M.: Weight preserving image registration for monitoring disease progression in lung CT. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 863–870. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-85990-1_104CrossRefGoogle Scholar
  8. 8.
    Huang, T.C., Hsiao, C.Y., Chien, C.R., Liang, J.A., Shih, T.C., Zhang, G.G.: IMRT treatment plans and functional planning with functional lung imaging from 4D-CT for thoracic cancer patients. Radiat. Oncol. 8(1), 3 (2013)CrossRefGoogle Scholar
  9. 9.
    Lavrenkov, K., et al.: A potential to reduce pulmonary toxicity: the use of perfusion spect with imrt for functional lung avoidance in radiotherapy of non-small cell lung cancer. Radiother. Oncol. 83(2), 156–162 (2007)CrossRefGoogle Scholar
  10. 10.
    Marks, L.B., Yu, X., Vujaskovic, Z., Small, W., Folz, R., Anscher, M.S.: Radiation-induced lung injury. Semin. Radiat. Oncol. 13(3), 333–345 (2003)CrossRefGoogle Scholar
  11. 11.
    National Cancer Institute, Bethesda, MD.: Cancer Stat Facts: Lung and bronchus cancer. Hypertext Document, January 2018. https://seer.cancer.gov/statfacts/html/lungb.html
  12. 12.
    Reinhardt, J.M., Ding, K., Cao, K., Christensen, G.E., Hoffman, E.A., Bodas, S.V.: Registration-based estimates of local lung tissue expansion compared to xenon ct measures of specific ventilation. Med. Image Anal. 12(6), 752–763 (2008)CrossRefGoogle Scholar
  13. 13.
    Siva, S., et al.: Ga-68 MAA perfusion 4d-PET/CT scanning allows for functional lung avoidance using conformal radiation therapy planning. Technol. Cancer Res. Treat. 15(1), 114–121 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Siva, S., et al.: High-resolution pulmonary ventilation and perfusion PET/CT allows for functionally adapted intensity modulated radiotherapy in lung cancer. Radiother. Oncol. 115(2), 157–162 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Vinogradskiy, Y., et al.: Regional lung function profiles of stage i and iii lung cancer patients: an evaluation for functional avoidance radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 95(4), 1273–1280 (2016)CrossRefGoogle Scholar
  16. 16.
    Yamamoto, T., et al.: Changes in regional ventilation during treatment and dosimetric advantages of ct ventilation image-guided radiotherapy for locally advanced lung cancer. Int. J. Radiat. Oncol. Biol. Phys. (2018)Google Scholar
  17. 17.
    Yamamoto, T., Kabus, S., Bal, M., Keall, P., Benedict, S., Daly, M.: The first patient treatment of computed tomography ventilation functional image-guided radiotherapy for lung cancer. Radiother. Oncol. 118(2), 227–231 (2016)CrossRefGoogle Scholar
  18. 18.
    Yamamoto, T., Kabus, S., Von Berg, J., Lorenz, C., Keall, P.J.: Impact of four-dimensional computed tomography pulmonary ventilation imaging-based functional avoidance for lung cancer radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 79(1), 279–288 (2011)CrossRefGoogle Scholar
  19. 19.
    Yaremko, B.P.: Reduction of normal lung irradiation in locally advanced non-small-cell lung cancer patients, using ventilation images for functional avoidance. Int. J. Radiat. Oncol. Biol. Phys. 68(2), 562–571 (2007).  https://doi.org/10.1016/j.ijrobp.2007.01.044. http://www.sciencedirect.com/science/article/B6T7X-4NCKJT4-P/2/f6e5dac7bef5f8ef7b954ccb8ed11972CrossRefGoogle Scholar
  20. 20.
    Yin, Y., Hoffman, E.A., Lin, C.L.: Mass preserving non-rigid registration of CT lung images using cubic B-spline. Med. Phys. 36(9), 4213–4222 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wei Shao
    • 1
    Email author
  • Taylor J. Patton
    • 2
  • Sarah E. Gerard
    • 3
  • Yue Pan
    • 1
  • Joseph M. Reinhardt
    • 3
  • John E. Bayouth
    • 4
  • Oguz C. Durumeric
    • 5
  • Gary E. Christensen
    • 1
    • 6
  1. 1.Department of Electrical and Computer EngineeringUniversity of IowaIowa CityUSA
  2. 2.Department of Medical PhysicsUniversity of Wisconsin-MadisonMadisonUSA
  3. 3.Department of Biomedical EngineeringUniversity of IowaIowa CityUSA
  4. 4.Department of Human OncologyUniversity of Wisconsin-MadisonMadisonUSA
  5. 5.Department of MathematicsUniversity of IowaIowa CityUSA
  6. 6.Department of Radiation OncologyUniversity of IowaIowa CityUSA

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