Comparison of image registration based measures of regional lung ventilation from dynamic spiral CT with Xe-CT.

PURPOSE Regional lung volume change as a function of lung inflation serves as an index of parenchymal and airway status as well as an index of regional ventilation and can be used to detect pathologic changes over time. In this paper, the authors propose a new regional measure of lung mechanics-the specific air volume change by corrected Jacobian. The authors compare this new measure, along with two existing registration based measures of lung ventilation, to a regional ventilation measurement derived from xenon-CT (Xe-CT) imaging. METHODS 4DCT and Xe-CT datasets from four adult sheep are used in this study. Nonlinear, 3D image registration is applied to register an image acquired near end inspiration to an image acquired near end expiration. Approximately 200 annotated anatomical points are used as landmarks to evaluate registration accuracy. Three different registration based measures of regional lung mechanics are derived and compared: the specific air volume change calculated from the Jacobian (SAJ); the specific air volume change calculated by the corrected Jacobian (SACJ); and the specific air volume change by intensity change (SAI). The authors show that the commonly used SAI measure can be derived from the direct SAJ measure by using the air-tissue mixture model and assuming there is no tissue volume change between the end inspiration and end expiration datasets. All three ventilation measures are evaluated by comparing to Xe-CT estimates of regional ventilation. RESULTS After registration, the mean registration error is on the order of 1 mm. For cubical regions of interest (ROIs) in cubes with size 20 mm × 20 mm × 20 mm, the SAJ and SACJ measures show significantly higher correlation (linear regression, average r(2) = 0.75 and r(2) = 0.82) with the Xe-CT based measure of specific ventilation (sV) than the SAI measure. For ROIs in slabs along the ventral-dorsal vertical direction with size of 150 mm × 8 mm × 40 mm, the SAJ, SACJ, and SAI all show high correlation (linear regression, average r(2) = 0.88, r(2) = 0.92, and r(2) = 0.87) with the Xe-CT based sV without significant differences when comparing between the three methods. The authors demonstrate a linear relationship between the difference of specific air volume change and difference of tissue volume in all four animals (linear regression, average r(2) = 0.86). CONCLUSIONS Given a deformation field by an image registration algorithm, significant differences between the SAJ, SACJ, and SAI measures were found at a regional level compared to the Xe-CT sV in four sheep that were studied. The SACJ introduced here, provides better correlations with Xe-CT based sV than the SAJ and SAI measures, thus providing an improved surrogate for regional ventilation.

[1]  Peter A Balter,et al.  Reduction of normal lung irradiation in locally advanced non-small-cell lung cancer patients, using ventilation images for functional avoidance. , 2007, International journal of radiation oncology, biology, physics.

[2]  J. Dichgans,et al.  Long-term side effects in irradiated patients with Hodgkin's disease. , 1977, International journal of radiation oncology, biology, physics.

[3]  Cristian Lorenz,et al.  Impact of four-dimensional computed tomography pulmonary ventilation imaging-based functional avoidance for lung cancer radiotherapy. , 2011, International journal of radiation oncology, biology, physics.

[4]  Eric A. Hoffman,et al.  Registration-based regional lung mechanical analysis: retrospectively reconstructed dynamic imaging versus static breath-hold image acquisition , 2009, Medical Imaging.

[5]  Tinsu Pan,et al.  Dynamic ventilation imaging from four-dimensional computed tomography , 2006, Physics in medicine and biology.

[6]  Milan Sonka,et al.  Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images , 2009, Int. J. Biomed. Imaging.

[7]  T. Pan Comparison of helical and cine acquisitions for 4D-CT imaging with multislice CT. , 2005, Medical physics.

[8]  Thomas Guerrero,et al.  Quantification of regional ventilation from treatment planning CT. , 2005, International journal of radiation oncology, biology, physics.

[9]  Thomas Guerrero,et al.  Ventilation from four-dimensional computed tomography: density versus Jacobian methods , 2010, Physics in medicine and biology.

[10]  Josien P. W. Pluim,et al.  Semi-automatic Reference Standard Construction for Quantitative Evaluation of Lung CT Registration , 2008, MICCAI.

[11]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[12]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[13]  B. Simon,et al.  Distribution of pulmonary ventilation using Xe-enhanced computed tomography in prone and supine dogs. , 2001, Journal of applied physiology.

[14]  Gary E. Christensen,et al.  Unifying Vascular Information in Intensity-Based Nonrigid Lung CT Registration , 2010, WBIR.

[15]  Marleen de Bruijne,et al.  First International Workshop on Pulmonary Image Analysis , 2008 .

[16]  Jiwoong Choi,et al.  Simulation of pulmonary air flow with a subject-specific boundary condition. , 2010, Journal of biomechanics.

[17]  Seungyong Lee,et al.  Injectivity Conditions of 2D and 3D Uniform Cubic B-Spline Functions , 2000, Graph. Model..

[18]  Wei Lu,et al.  Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry. , 2007, Medical physics.

[19]  E L Ritman,et al.  Effect of body orientation on regional lung expansion in dog and sloth. , 1985, Journal of applied physiology.

[20]  Gary E. Christensen,et al.  Tissue volume and vesselness measure preserving nonrigid registration of lung CT images , 2010, Medical Imaging.

[21]  Hans M. Hertz,et al.  Ultrasonic trapping in capillaries for trace-amount biomedical analysis , 2001 .

[22]  P. J. Keall,et al.  Potential radiotherapy improvements with respiratory gating , 2009, Australasian Physics & Engineering Sciences in Medicine.

[23]  E. Hoffman,et al.  Mass preserving nonrigid registration of CT lung images using cubic B-spline. , 2009, Medical physics.

[24]  Eric A. Hoffman,et al.  Local tissue-weight-based nonrigid registration of lung images with application to regional ventilation , 2009, Medical Imaging.

[25]  Eric A. Hoffman,et al.  Differences in regional wash-in and wash-out time constants for xenon-CT ventilation studies , 2005, Respiratory Physiology & Neurobiology.

[26]  Eric A Hoffman,et al.  CT-measured regional specific volume change reflects regional ventilation in supine sheep. , 2008, Journal of applied physiology.

[27]  G. Christensen,et al.  A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing. , 2003, Medical physics.

[28]  Eric A. Hoffman,et al.  Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation , 2008, Medical Image Anal..

[29]  Brett A. Simon,et al.  Non-Invasive Imaging of Regional Lung Function using X-Ray Computed Tomography , 2004, Journal of Clinical Monitoring and Computing.

[30]  Kai Ding,et al.  4DCT-based measurement of changes in pulmonary function following a course of radiation therapy. , 2010, Medical physics.

[31]  Eric A Hoffman,et al.  Subsecond multisection CT of regional pulmonary ventilation. , 2002, Academic radiology.

[32]  Eric A. Hoffman,et al.  Evaluation of Lobar Biomechanics during Respiration Using Image Registration , 2009, MICCAI.

[33]  Joseph M. Reinhardt,et al.  Automated measurement of retinal blood vessel tortuosity , 2010, Medical Imaging.

[34]  G. Christensen,et al.  Intensity-and-Landmark-Driven , Inverse Consistent , B-Spline Registration and Analysis for Lung Imagery , 2009 .

[35]  Milan Sonka,et al.  Automated segmentation of pulmonary vascular tree from 3D CT images , 2004, SPIE Medical Imaging.

[36]  Walter L. Smith Probability and Statistics , 1959, Nature.

[37]  K. Ding Registration-based regional lung mechanical analysis , 2008 .