Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. B. A. Simon, D. W. Kaczka, A. A. Bankier, and G. Parraga, “What can computed tomography and magnetic resonance imaging tell us about ventilation?” J. Appl. Physiol. 113, 647657 (2012).
2. T. Guerrero, K. Sanders, E. Castillo, Y. Zhang, L. Bidaut, T. Pan, and R. Komaki, “Dynamic ventilation imaging from four-dimensional computed tomography,” Phys. Med. Biol. 51, 777791 (2006).
3. Y. Vinogradskiy, R. Castillo, E. Castillo, S. L. Tucker, Z. Liao, T. Guerrero, and M. K. Martel, “Use of 4-dimensional computed tomography-based ventilation imaging to correlate lung dose and function with clinical outcomes,” Int. J. Radiat. Oncol., Biol., Phys. 86, 366371 (2013).
4. T. Yamamoto, S. Kabus, J. von Berg, C. Lorenz, and P. J. Keall, “Impact of four-dimensional computed tomography pulmonary ventilation imaging-based functional avoidance for lung cancer radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 79, 279288 (2011).
5. M. S. Hofman, J. M. Beauregard, T. W. Barber, O. C. Neels, P. Eu, and R. J. Hicks, “68Ga PET/CT ventilation-perfusion imaging for pulmonary embolism: A pilot study with comparison to conventional scintigraphy,” J. Nucl. Med. 52, 15131519 (2011).
6. J. Callahan, M. S. Hofman, S. Siva, T. Kron, M. E. Schneider, D. Binns, P. Eu, and R. J. Hicks, “High-resolution imaging of pulmonary ventilation and perfusion with Ga-VQ respiratory gated (4-D) PET/CT,” Eur. J. Nucl. Med. Mol. Imaging (2013). [E-pub ahead of print].
7. F.-M. Kong, R. Ten Haken, A. Eisbruch, and T. S. Lawrence, “Non-small cell lung cancer therapy-related pulmonary toxicity: An update on radiation pneumonitis and fibrosis,” Semin. Oncol. 32, 4254 (2005).
8. S. S. Yom, Z. Liao, H. H. Liu, S. L. Tucker, C. S. Hu, X. Wei, X. Wang, S. Wang, R. Mohan, J. D. Cox, and R. Komaki, “Initial evaluation of treatment-related pneumonitis in advanced-stage non-small-cell lung cancer patients treated with concurrent chemotherapy and intensity-modulated radiotherapy,” Int. J. Radiat. Oncol., Biol., Phys. 68, 94102 (2007).
9. S. Sura, V. Gupta, E. Yorke, A. Jackson, H. Amols, and K. E. Rosenzweig, “Intensity-modulated radiation therapy (IMRT) for inoperable non-small cell lung cancer: The Memorial Sloan-Kettering Cancer Center (MSKCC) experience,” Radiother. Oncol. 87, 1723 (2008).
10. L. B. Marks, S. M. Bentzen, J. O. Deasy, F. M. Kong, J. D. Bradley, I. S. Vogelius, I. El Naqa, J. L. Hubbs, J. V. Lebesque, R. D. Timmerman, M. K. Martel, and A. Jackson, “Radiation dose-volume effects in the lung,” Int. J. Radiat. Oncol., Biol., Phys. 76, S70S76 (2010).
11. K. De Jaeger, Y. Seppenwoolde, L. J. Boersma, S. H. Muller, P. Baas, J. S. Belderbos, and J. V. Lebesque, “Pulmonary function following high-dose radiotherapy of non-small-cell lung cancer,” Int. J. Radiat. Oncol., Biol., Phys. 55, 13311340 (2003).
12. P. A. Lind, L. B. Marks, D. Hollis, M. Fan, S. M. Zhou, M. T. Munley, T. D. Shafman, R. J. Jaszczak, and R. E. Coleman, “Receiver operating characteristic curves to assess predictors of radiation-induced symptomatic lung injury,” Int. J. Radiat. Oncol., Biol., Phys. 54, 340347 (2002).
13. D. Wang, J. Sun, J. Zhu, X. Li, Y. Zhen, and S. Sui, “Functional dosimetric metrics for predicting radiation-induced lung injury in non-small cell lung cancer patients treated with chemoradiotherapy,” Radiat. Oncol. 7, 19 (2012).
14. I. W. Gayed, J. Chang, E. E. Kim, R. Nunez, B. Chasen, H. H. Liu, K. Kobayashi, Y. Zhang, Z. Liao, S. Gohar, M. Jeter, L. Henderson, W. Erwin, and R. Komaki, “Lung perfusion imaging can risk stratify lung cancer patients for the development of pulmonary complications after chemoradiation,” J. Thorac. Oncol. 3, 858864 (2008).
15. T. Kimura, I. Nishibuchi, Y. Murakami, M. Kenjo, Y. Kaneyasu, and Y. Nagata, “Functional image-guided radiotherapy planning in respiratory-gated intensity-modulated radiotherapy for lung cancer patients with chronic obstructive pulmonary disease,” Int. J. Radiat. Oncol., Biol., Phys. 82, e663e670 (2012).
16. S. M. McGuire, L. B. Marks, F. F. Yin, and S. K. Das, “A methodology for selecting the beam arrangement to reduce the intensity-modulated radiation therapy (IMRT) dose to the SPECT-defined functioning lung,” Phys. Med. Biol. 55, 403416 (2010).
17. B. P. Yaremko, T. M. Guerrero, J. Noyola-Martinez, R. Guerra, D. G. Lege, L. T. Nguyen, P. A. Balter, J. D. Cox, and R. Komaki, “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, 562571 (2007).
18. K. Suga, “Technical and analytical advances in pulmonary ventilation SPECT with xenon-133 gas and Tc-99m-Technegas,” Ann. Nucl. Med. 16, 303310 (2002).
19. J. Bayouth, K. Du, G. Christensen, B. Smith, J. Buatti, and J. Reinhardt, “Establishing a relationship between radiosensitivity of lung tissue and ventilation,” Int. J. Radiat. Oncol., Biol., Phys. 84, S31S32 (2012).
20. Y. Y. Vinogradskiy, R. Castillo, E. Castillo, A. Chandler, M. K. Martel, and T. Guerrero, “Use of weekly 4DCT-based ventilation maps to quantify changes in lung function for patients undergoing radiation therapy,” Med. Phys. 39, 289298 (2012).
21. M. K. Fuld, R. B. Easley, O. I. Saba, D. Chon, J. M. Reinhardt, E. A. Hoffman, and B. A. Simon, “CT-measured regional specific volume change reflects regional ventilation in supine sheep,” J. Appl. Physiol. 104, 11771184 (2008).
22. K. Ding, K. Cao, M. K. Fuld, K. Du, G. E. Christensen, E. A. Hoffman, and J. M. Reinhardt, “Comparison of image registration based measures of regional lung ventilation from dynamic spiral CT with Xe-CT,” Med. Phys. 39, 50845098 (2012).
23. L. Mathew, A. Wheatley, R. Castillo, E. Castillo, G. Rodrigues, T. Guerrero, and G. Parraga, “Hyperpolarized (3)He magnetic resonance imaging: Comparison with four-dimensional x-ray computed tomography imaging in lung cancer,” Acad. Radiol. 19, 15461553 (2012).
24. R. Castillo, E. Castillo, J. Martinez, and T. Guerrero, “Ventilation from four-dimensional computed tomography: Density versus Jacobian methods,” Phys. Med. Biol. 55, 46614685 (2010).
25. R. Castillo, E. Castillo, M. McCurdy, D. R. Gomez, A. M. Block, D. Bergsma, S. Joy, and T. Guerrero, “Spatial correspondence of 4D CT ventilation and SPECT pulmonary perfusion defects in patients with malignant airway stenosis,” Phys. Med. Biol. 57, 18551871 (2012).
26. T. Yamamoto, S. Kabus, J. von Berg, C. Lorenz, M. L. Goris, B. W. Loo Jr., and P. Keall, “Evaluation of four-dimensional (4D) computed tomography (CT) pulmonary ventilation imaging by comparison with single photon emission computed tomography (SPECT) scans for a lung cancer patient,” in Proceedings of the Third International Workshop on Pulmonary Image Analysis, MICCAI, Beijing, China, 2010 (2010), pp. 117128,
27. J. Palmer, U. Bitzen, B. Jonson, and M. Bajc, “Comprehensive ventilation/perfusion SPECT,” J. Nucl. Med. 42, 12881294 (2001).
28. T. Yamamoto, S. Kabus, T. Klinder, C. Lorenz, J. von Berg, T. Blaffert, B. W. Loo Jr., and P. J. Keall, “Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions,” Phys. Med. Biol. 56, 22792298 (2011).
29. T. Yamamoto, S. Kabus, T. Klinder, J. von Berg, C. Lorenz, B. W. Loo Jr., and P. J. Keall, “Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics,” Med. Phys. 38, 13481358 (2011).
30. B. A. Simon, “Non-invasive imaging of regional lung function using x-ray computed tomography,” J. Clin. Monit. Comput. 16, 433442 (2000).
31. J. M. Reinhardt, K. Ding, K. Cao, G. E. Christensen, E. A. Hoffman, and S. V. Bodas, “Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation,” Med. Image Anal. 12, 752763 (2008).
32. K. Du, J. E. Bayouth, K. Ding, G. E. Christensen, K. Cao, and J. M. Reinhardt, “Reproducibility of intensity-based estimates of lung ventilation,” Med. Phys. 40, 063504 (18pp.) (2013).
33. J. B. Borges, I. Velikyan, B. Langstrom, J. Sorensen, J. Ulin, E. Maripuu, M. Sandstrom, C. Widstrom, and G. Hedenstierna, “Ventilation distribution studies comparing Technegas and ‘Gallgas' using 68GaCl3 as the label,” J. Nucl. Med. 52, 206209 (2011).
34. T. Isawa, B. T. Lee, and K. Hiraga, “High-resolution electron microscopy of technegas and pertechnegas,” Nucl. Med. Commun. 17, 147152 (1996).
35. K. Latifi, T. Huang, V. Feygelman, M. M. Budzevich, E. G. Moros, T. J. Dilling, C. W. Stevens, W. Van Elmpt, A. Dekker, and G. G. Zhang, “Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data,” Phys. Med. Biol. 58, 76617672 (2013).
36. J. M. Reinhardt, V. Chu, G. Hamarneh, and J. P. W. Pluim, “MATLAB-ITK interface for medical image filtering, segmentation, and registration,” Proc. SPIE 6144, 61443T (2006).
37. P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 11161128 (2006).
38. D. A. Low and J. F. Dempsey, “Evaluation of the gamma dose distribution comparison method,” Med. Phys. 30, 24552464 (2003).
39. K. Murphy, B. van Ginneken, S. Klein, M. Staring, B. J. de Hoop, M. A. Viergever, and J. P. Pluim, “Semi-automatic construction of reference standards for evaluation of image registration,” Med. Image Anal. 15, 7184 (2011).
40. K. Murphy, B. van Ginneken, J. Reinhardt, S. Kabus, K. Ding, X. Deng, and J. Pluim, “Evaluation of methods for pulmonary image registration: The EMPIRE10 study,” in Grand Challenges in Medical Image Analysis 2010 (MICCAI, Beijing, China, 2010).
41. T. Yamamoto, S. Kabus, J. von Berg, C. Lorenz, M. P. Chung, J. C. Hong, B. W. Loo Jr., and P. J. Keall, “Reproducibility of four-dimensional computed tomography-based lung ventilation imaging,” Acad. Radiol. 19, 15541565 (2012).
42. N. W. Morrell, B. K. Wignall, T. Biggs, and W. A. Seed, “Collateral ventilation and gas exchange in emphysema,” Am. J. Respir. Crit. Care Med. 150, 635641 (1994).
43. T. Yamamoto, U. Langner, B. W. Loo, J. Shen, and P. J. Keall, “Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients,” Int. J. Radiat Oncol., Biol., Phys. 72, 12501258 (2008).
44. T. Yamamoto, S. Kabus, C. Lorenz, E. Johnston, P. G. Maxim, M. Diehn, N. Eclov, C. Barquero, B. W. Loo, and P. J. Keall, “4D CT lung ventilation images are affected by the 4D CT sorting method,” Med. Phys. 40, 101907 (9pp.) (2013).
45. D. A. Low, B. M. White, P. P. Lee, D. H. Thomas, S. Gaudio, S. S. Jani, X. Wu, and J. M. Lamb, “A novel CT acquisition and analysis technique for breathing motion modeling,” Phys. Med. Biol. 58, L31L36 (2013).
46. E. Castillo, R. Castillo, J. Martinez, M. Shenoy, and T. Guerrero, “Four-dimensional deformable image registration using trajectory modeling,” Phys. Med. Biol. 55, 305327 (2010).
47. Z. Wu, E. Rietzel, V. Boldea, D. Sarrut, and G. C. Sharp, “Evaluation of deformable registration of patient lung 4DCT with subanatomical region segmentations,” Med. Phys. 35, 775781 (2008).
48. V. Delmon, S. Rit, R. Pinho, and D. Sarrut, “Registration of sliding objects using direction dependent B-splines decomposition,” Phys. Med. Biol. 58, 13031314 (2013).

Data & Media loading...


Article metrics loading...



CT ventilation imaging is a novel functional lung imaging modality based on deformable image registration. The authors present the first validation study of CT ventilation using positron emission tomography with68Ga-labeled nanoparticles (PET-Galligas). The authors quantify this agreement for different CT ventilation metrics and PET reconstruction parameters.

PET-Galligas ventilation scans were acquired for 12 lung cancer patients using a four-dimensional (4D) PET/CT scanner. CT ventilation images were then produced by applying B-spline deformable image registration between the respiratory correlated phases of the 4D-CT. The authors test four ventilation metrics, two existing and two modified. The two existing metrics model mechanical ventilation (alveolar air-flow) based on Hounsfield unit (HU) change ( ) or Jacobian determinant of deformation ( ). The two modified metrics incorporate a voxel-wise tissue-density scaling (ρ and ρ ) and were hypothesized to better model the physiological ventilation. In order to assess the impact of PET image quality, comparisons were performed using both standard and respiratory-gated PET images with the former exhibiting better signal. Different median filtering kernels (σ = 0 or 3 mm) were also applied to all images. As in previous studies, similarity metrics included the Spearman correlation coefficient within the segmented lung volumes, and Dice coefficient for the (0 − 20)th functional percentile volumes.

The best agreement between CT and PET ventilation was obtained comparing standard PET images to the density-scaled HU metric (ρ ) with σ = 3 mm. This leads to correlation values in the ranges 0.22 ⩽ ⩽ 0.76 and 0.38 ⩽ ⩽ 0.68, with and averaged over the 12 patients. Compared to Jacobian-based metrics, HU-based metrics lead to statistically significant improvements in and ( < 0.05), with density scaled metrics also showing higher than for unscaled versions ( < 0.02). and were also sensitive to image quality, with statistically significant improvements using standard (as opposed to gated) PET images and with application of median filtering.

The use of modified CT ventilation metrics, in conjunction with PET-Galligas and careful application of image filtering has resulted in improved correlation compared to earlier studies using nuclear medicine ventilation. However, CT ventilation and PET-Galligas do not always provide the same functional information. The authors have demonstrated that the agreement can improve for CT ventilation metrics incorporating a tissue density scaling, and also with increasing PET image quality. CT ventilation imaging has clear potential for imaging regional air volume change in the lung, and further development is warranted.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd