Rationale and Objectives
Pulmonary functional imaging using four-dimensional x-ray computed tomographic (4DCT) imaging and hyperpolarized 3 He magnetic resonance imaging (MRI) provides regional lung function estimates in patients with lung cancer in whom pulmonary function measurements are typically dominated by tumor burden. The aim of this study was to evaluate the quantitative spatial relationship between 4DCT and hyperpolarized 3 He MRI ventilation maps.
Materials and Methods
Eleven patients with lung cancer provided written informed consent to 4DCT imaging and MRI performed within 11 ± 14 days. Hyperpolarized 3 He MRI was acquired in breath-hold after inhalation from functional residual capacity of 1 L hyperpolarized 3 He, whereas 4DCT imaging was acquired over a single tidal breath of room air. For hyperpolarized 3 He MRI, the percentage ventilated volume was generated using semiautomated segmentation; for 4DCT imaging, pulmonary function maps were generated using the correspondence between identical tissue elements at inspiratory and expiratory phases to generate percentage ventilated volume.
Results
After accounting for differences in image acquisition lung volumes ( 3 He MRI: 1.9 ± 0.5 L ipsilateral, 2.3 ± 0.7 L contralateral; 4DCT imaging: 1.2 ± 0.3 L ipsilateral, 1.3 ± 0.4 L contralateral), there was no significant difference in percentage ventilated volume between hyperpolarized 3 He MRI (72 ± 11% ipsilateral, 79 ± 12% contralateral) and 4DCT imaging (74 ± 3% ipsilateral, 75 ± 4% contralateral). Spatial correspondence between 4DCT and 3 He MRI ventilation was evaluated using the Dice similarity coefficient index (ipsilateral, 86 ± 12%; contralateral, 88 ± 12%).
Conclusions
Despite rather large differences in image acquisition breathing maneuvers, good spatial and significant quantitative agreement was observed for ventilation maps on hyperpolarized 3 He MRI and 4DCT imaging, suggesting that pulmonary regions with good lung function are similar between modalities in this small group of patients with lung cancer.
Thoracic imaging functional measurements and maps have been evaluated for use in lung cancer radiation therapy planning with single-photon emission computed tomography (SPECT) , high-resolution four-dimensional (4D) x-ray computed tomographic (4DCT) imaging , and hyperpolarized noble gas magnetic resonance imaging (MRI) . These studies support the use of regional thoracic functional measurements for lung cancer radiation treatment plans and have resulted in reduced radiation dose to well-functioning lung without dose decreases to the treatment target volume . Lung cancer radiation therapy planning typically relies on anatomic measurements on CT such as gross tumor volume and anatomic location, as well as radiation dose parameters such as the volume fraction of the lung receiving ≥20 Gy and mean lung dose. On the basis of these parameters, the 5-year survival rate for stage III small-cell lung cancer and non-small-cell lung cancer is still very low (<20%) . Thus, there is a critical need to improve lung cancer radiation therapy outcomes, and this is driving research that incorporates functional imaging measurements within therapy planning for lung cancer .
In this regard, the incorporation of SPECT for lung cancer radiation therapy planning has been promising, but there are some inherent limitations that may preclude routine clinical use, mainly related to image artifacts stemming from radiolabeled tracers depositing in the major airways , requiring significant postprocessing to remove and sometimes resulting in distortion of the underlying ventilation signal. Hyperpolarized 3 He MRI provides an alternative to SPECT , but the availability of 3 He is inherently limited, and the global supply is rapidly declining and expensive , limiting the potential for its translation to clinical use. Hyperpolarized 129 Xe MRI is another alternative , but it is less developed than 3 He MRI and is currently in the early stages of clinical research applications . In contrast, thoracic CT is readily available at most clinical centers, and pulmonary function maps can be generated from computed tomographic images acquired during maximum inhalation and exhalation as well as tidal breathing . Preliminary results suggest that 4DCT ventilation volumes correlate well with tidal volumes and with changes in contoured lung volumes between maximum inspiration and expiration images . Furthermore, 4DCT imaging is increasingly being used as a standard of care to evaluate primary tumor motion prior to radiation therapy and therefore would not require additional imaging to incorporate functional images into radiation treatment plans . Although 4DCT imaging appears to be the most practical tool for incorporating lung ventilation maps into the treatment planning regime for lung cancer, before clinical translation can occur, a thorough and comparative evaluation of 4DCT imaging with other imaging methods is required to validate 4DCT functional maps.
Get Radiology Tree app to read full this article<
Materials and methods
Research Subjects
Get Radiology Tree app to read full this article<
3 He MRI
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
4DCT Acquisition
Get Radiology Tree app to read full this article<
3 He Magnetic Resonance Image Analysis
Get Radiology Tree app to read full this article<
4DCT Image Analysis
Get Radiology Tree app to read full this article<
ΔV/Vairexhale=1000×(H¯¯¯inhale−HUexhale)/HUexhale/(1000+H¯¯¯inhale), Δ
V
/
V
air
exhale
=
1000
×
(
H
¯
inhale
−
HU
exhale
)
/
HU
exhale
/
(
1000
+
H
¯
inhale
)
,
where H¯¯¯inhale H
¯
inhale represents the mean HU of the tissue volume mapped into the exhale voxel, with the corresponding computed tomographic number given by HUexhale HU
exhale . The specific ventilation was inherently coregistered to the maximum exhalation phase computed tomographic image and calculated for all voxels defined by corresponding binary lung parenchyma masks. Image segmentation for the generation of lung masks was previously described and was performed using a global histogram threshold to define the lung parenchyma. Three-dimensional region-growing techniques were used to extract and remove the main-stem bronchi and pulmonary vasculature from the images . We derived 4DCT VV from the 4DCT ventilation images, whereby the 4DCT ventilation map was segmented into five clusters using three-dimensional k-means cluster analysis as described previously for hyperpolarized 3 He MRI segmentation , where cluster 1, representing the lowest ventilation intensity, was defined as ventilation defect and clusters 2 to 5 were defined as ventilated voxels. The maximum exhalation phase computed tomographic images were used to calculate the TCV. Four-dimensional computed tomographic PVV was calculated as the ratio of VV to TCV. The 4DCT ventilation map was reconstructed from the axial (acquisition plane) to the coronal plane. Four-dimensional computed tomographic ventilation map voxels were averaged to match resolution of the 3 He ventilation map.
Get Radiology Tree app to read full this article<
Comparison of 4DCT and 3 He MRI
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
s=2|X∩Y||X|+|Y|, s
=
2
|
X
∩
Y
|
|
X
|
+
|
Y
|
,
where X is the TCV 4DCT slice mask and Y is the TCV 3 He slice mask. The Dice coefficient calculation was repeated to evaluate the spatial correspondence of 4DCT ipsilateral VV ( X in the above equation) to 3 He MRI ipsilateral VV ( Y in the above equation) and as well for the contralateral side.
Get Radiology Tree app to read full this article<
Statistical Analysis
Get Radiology Tree app to read full this article<
Results
Get Radiology Tree app to read full this article<
Table 1
Subject Demographics
Subject Age (y) Sex Pretreatment FEV 1 (% Predicted) Pack-Years Previously Diagnosed Lung Disease Tumor Location 1 68 M 68 50 COPD LUL 2 62 F 69 40 – RUL 3 51 F 26 78 COPD RUL 4 77 F 76 1 – RUL 5 47 F 86 32 – LUL 6 69 F 89 25 – LUL 7 73 F 78 20 – RLL 8 70 F 56 84 – RLL 9 66 F 45 55 COPD RUL 10 61 F 84 5 – RLL 11 62 F 85 25 – RUL Mean 64 70 37 SD 9 19 27
COPD, chronic obstructive pulmonary disease; FEV 1 , forced expiratory volume in 1 second; LUL, left upper lung; RUL, right upper lung; RLL, right lower lung; SD, standard deviation.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Table 2
Ventilation Measurements and Dice Coefficients for Hyperpolarized 3 He MRI and 4DCT
Subject Lung 3 He MRI 4DCT 3 He MRI/4DCT VV (L) PVV (%) VV (L) PVV (%) TCV Dice Coefficient (%) VV Dice Coefficient (%) 1 I 1.8 63 1.7 76 88 69 C 3.6 81 2.5 80 91 73 2 I 1.9 76 1.3 75 91 93 C 1.9 84 1.0 77 89 94 3 I 1.3 53 1.4 71 91 80 C 1.1 49 1.2 73 92 76 4 I 2.0 80 1.2 76 88 90 C 2.3 76 1.4 80 91 94 5 I 1.7 79 1.1 80 89 95 C 2.1 79 1.6 83 92 94 6 I 1.7 75 0.8 74 87 94 C 2.9 87 1.1 73 90 95 7 I 2.6 86 1.0 74 91 94 C 2.1 88 1.2 73 92 95 8 I 2.0 77 1.3 74 91 75 C 1.1 72 0.6 68 87 66 9 I 1.9 70 1.5 72 89 90 C 1.6 72 1.1 72 88 89 10 I 1.6 54 1.5 77 85 75 C 3.1 89 1.1 84 88 96 11 I 2.8 85 1.1 73 90 94 C 2.8 89 0.8 69 89 96 Mean I 1.9 72 1.2 74 89 86 C 2.3 79 1.3 75 90 88 SD I 0.5 11 0.3 3 2 10 C 0.7 12 0.4 4 2 11
C, contralateral lung; 4DCT, four-dimensional computed tomography; I, ipsilateral lung; MRI, magnetic resonance imaging; PVV, percentage ventilated volume; SD, standard deviation; TCV, thoracic cavity volume; VV, ventilated volume.
Get Radiology Tree app to read full this article<
Discussion
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Conclusions
Get Radiology Tree app to read full this article<
Acknowledgments
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
References
1. Shioyama Y., Jang S.Y., Liu H.H., et. al.: Preserving functional lung using perfusion imaging and intensity-modulated radiation therapy for advanced-stage non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2007; 68: pp. 1349-1358.
2. Yaremko B.P., Guerrero T.M., Noyola-Martinez J., et. al.: 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 2007; 68: pp. 562-571.
3. Ding K., Bayouth J.E., Buatti J.M., et. al.: 4DCT-based measurement of changes in pulmonary function following a course of radiation therapy. Med Phys 2010; 37: pp. 1261-1272.
4. Vinogradskiy Y.Y., Castillo R., Castillo E., et. al.: Use of weekly 4DCT-based ventilation maps to quantify changes in lung function for patients undergoing radiation therapy. Med Phys 2012; 39: pp. 289-298.
5. Hodge C.W., Tome W.A., Fain S.B., et. al.: On the use of hyperpolarized helium MRI for conformal avoidance lung radiotherapy. Med Dosim 2010; 35: pp. 297-303.
6. Ireland R.H., Bragg C.M., McJury M., et. al.: Feasibility of image registration and intensity-modulated radiotherapy planning with hyperpolarized helium-3 magnetic resonance imaging for non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2007; 68: pp. 273-281.
7. Mountain C.F.: Staging classification of lung cancer. A critical evaluation. Clin Chest Med 2002; 23: pp. 103-121.
8. Jogi J., Jonson B., Ekberg M., et. al.: Ventilation-perfusion SPECT with 99mTc-DTPA versus Technegas: a head-to-head study in obstructive and nonobstructive disease. J Nucl Med 2010; 51: pp. 735-741.
9. Mathew L., Gaede S., Wheatley A., et. al.: Detection of longitudinal lung structural and functional changes after diagnosis of radiation-induced lung injury using hyperpolarized 3 He magnetic resonance imaging. Med Phys 2010; 37: pp. 22-31.
10. Fain S., Schiebler M.L., McCormack D.G., et. al.: Imaging of lung function using hyperpolarized helium-3 magnetic resonance imaging: review of current and emerging translational methods and applications. J Magn Reson Imaging 2010; 32: pp. 1398-1408.
11. Mugler J.P., Driehuys B., Brookeman J.R., et. al.: MR imaging and spectroscopy using hyperpolarized 129Xe gas: preliminary human results. Magn Reson Med 1997; 37: pp. 809-815.
12. Patz S., Muradian I., Hrovat M.I., et. al.: Human pulmonary imaging and spectroscopy with hyperpolarized 129Xe at 0.2T. Acad Radiol 2008; 15: pp. 713-727.
13. Mugler J.P., Altes T.A., Ruset I.C., et. al.: Simultaneous magnetic resonance imaging of ventilation distribution and gas uptake in the human lung using hyperpolarized xenon-129. Proc Natl Acad Sci U S A 2010; 107: pp. 21707-21712.
14. Shukla Y., Wheatley A., Kirby M., et. al.: Hyperpolarized 129 Xe magnetic resonance imaging: tolerability in healthy volunteers and subjects with pulmonary disease. Acad Radiol 2012; 19: pp. 941-951.
15. Kirby M., Svenningsen S., Owrangi A., et. al.: Hyperpolarized 3 He and 129 Xe MR imaging in healthy volunteers and patients with chronic obstructive pulmonary disease. Radiol 2012; [Epub ahead of print].
16. Guerrero T., Sanders K., Noyola-Martinez J., et. al.: Quantification of regional ventilation from treatment planning CT. Int J Radiat Oncol Biol Phys 2005; 62: pp. 630-634.
17. Castillo R., Castillo E., Martinez J., et. al.: Ventilation from four-dimensional computed tomography: density versus Jacobian methods. Phys Med Biol 2010; 55: pp. 4661-4685.
18. Guerrero T., Sanders K., Castillo E., et. al.: Dynamic ventilation imaging from four-dimensional computed tomography. Phys Med Biol 2006; 51: pp. 777-791.
19. Castillo R., Castillo E., McCurdy M., et. al.: Spatial correspondence of 4D CT ventilation and SPECT pulmonary perfusion defects in patients with malignant airway stenosis. Phys Med Biol 2012; 57: pp. 1855-1871.
20. Aysola R., de Lange E.E., Castro M., et. al.: Demonstration of the heterogeneous distribution of asthma in the lungs using CT and hyperpolarized helium-3 MRI. J Magn Reson Imaging 2010; 32: pp. 1379-1387.
21. Stavngaard T., Sogaard L.V., Batz M., et. al.: Progression of emphysema evaluated by MRI using hyperpolarized 3 He (HP 3 He) measurements in patients with alpha-1-antitrypsin (A1AT) deficiency compared with CT and lung function tests. Acta Radiol 2009; 50: pp. 1019-1026.
22. Rabe K.F., Hurd S., Anzueto A., et. al.: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2007; 176: pp. 532-555.
23. Mathew L., Evans A., Ouriadov A., et. al.: Hyperpolarized 3 He magnetic resonance imaging of chronic obstructive pulmonary disease: reproducibility at 3.0 tesla. Acad Radiol 2008; 15: pp. 1298-1311.
24. Evans A., McCormack D., Ouriadov A., et. al.: Anatomical distribution of 3 He apparent diffusion coefficients in severe chronic obstructive pulmonary disease. J Magn Reson Imaging 2007; 26: pp. 1537-1547.
25. Parraga G., Ouriadov A., Evans A., et. al.: Hyperpolarized 3 He ventilation defects and apparent diffusion coefficients in chronic obstructive pulmonary disease: preliminary results at 3.0 tesla. Invest Radiol 2007; 42: pp. 384-391.
26. Louie A.V., Rodrigues G., Olsthoorn J., et. al.: Inter-observer and intra-observer reliability for lung cancer target volume delineation in the 4D-CT era. Radiother Oncol 2010; 95: pp. 166-171.
27. Kirby M., Heydarian M., Svenningsen S., et. al.: Hyperpolarized 3 He magnetic resonance functional imaging semiautomated segmentation. Acad Radiol 2012; 19: pp. 141-152.
28. Kirby M., Mathew L., Heydarian M., et. al.: Chronic obstructive pulmonary disease: quantification of bronchodilator effects by using hyperpolarized 3 He MR imaging. Radiology 2011; 261: pp. 283-292.
29. Castillo E., Castillo R., Martinez J., et. al.: Four-dimensional deformable image registration using trajectory modeling. Phys Med Biol 2010; 55: pp. 305-327.
30. Dice L.: Measures of the amount of ecologic association between species. Ecology 1945; 26: pp. 297-302.
31. Du K., Bayouth J.E., Cao K., et. al.: Reproducibility of registration-based measures of lung tissue expansion. Med Phys 2012; 39: pp. 1595-1608.
32. Mori S., Kanematsu N., Asakura H., et. al.: Four-dimensional lung treatment planning in layer-stacking carbon ion beam treatment: comparison of layer-stacking and conventional ungated/gated irradiation. Int J Radiat Oncol Biol Phys 2011; 80: pp. 597-607.