Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics

Purpose: A novel pulmonary ventilation imaging technique based on four-dimensional (4D) CT has advantages over existing techniques and could be used for functional avoidance in radiotherapy. There are various deformable image registration (DIR) algorithms and two classes of ventilation metric that c...

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Bibliographic Details
Published in:Medical physics (Lancaster) 2011-03, Vol.38 (3), p.1348-1358
Main Authors: Yamamoto, Tokihiro, Kabus, Sven, Klinder, Tobias, von Berg, Jens, Lorenz, Cristian, Loo, Billy W., Keall, Paul J.
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Algorithms
Cancer
Cohort Studies
Computed tomography
computerised tomography
deformable image registration
Female
four-dimensional (4D) CT
Four-Dimensional Computed Tomography - methods
functional imaging
Humans
Image Processing, Computer-Assisted - methods
image registration
Jacobians
lung
Lungs
Magnetic resonance imaging
Male
medical image processing
Medical imaging
Middle Aged
Nuclear medicine imaging
Pulmonary Ventilation
radiation therapy
Registration
Retrospective Studies
Single photon emission computed tomography
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Summary:Purpose: A novel pulmonary ventilation imaging technique based on four-dimensional (4D) CT has advantages over existing techniques and could be used for functional avoidance in radiotherapy. There are various deformable image registration (DIR) algorithms and two classes of ventilation metric that can be used for 4D-CT ventilation imaging, each yielding different images. The purpose of this study was to quantify the variability of the 4D-CT ventilation to DIR algorithms and metrics. Methods: 4D-CT ventilation images were created for 12 patients using different combinations of two DIR algorithms, volumetric ( DIR vol ) and surface-based ( DIR sur ) , yielding two displacement vector fields (DVFs) per patient ( DVF vol and DVF sur ), and two metrics, Hounsfield unit (HU) change ( V HU ) and Jacobian determinant of deformation ( V Jac ) , yielding four ventilation image sets ( V HU vol , V HU sur , V Jac vol , and V Jac sur ). First, DVF vol and DVF sur were compared visually and quantitatively to the length of 3D displacement vector difference. Second, four ventilation images were compared based on voxel-based Spearman’s rank correlation coefficients and coefficients of variation as a measure of spatial heterogeneity. V HU vol was chosen as the reference for the comparison. Results: The mean length of 3D vector difference between DVF vol and DVF sur was 2.0 ± 1.1   mm on average, which was smaller than the voxel dimension of the image set and the variations. Visually, the reference V HU vol demonstrated similar regional distributions with V HU sur ; the reference, however, was markedly different from V Jac vol and V Jac sur . The correlation coefficients of V HU vol with V HU sur , V Jac vol , and V Jac sur were 0.77 ± 0.06 , 0.25 ± 0.06 , and 0.15 ± 0.07 , respectively, indicating that the metric introduced larger variations in the ventilation images than the DIR algorithm. The spatial heterogeneities for V HU vol , V HU sur , V Jac vol , and V Jac sur were 1.8 ± 1.6 , 1.8 ± 1.5 ( p = 0.85 ) , 0.6 ± 0.2 ( p = 0.02 ) , and 0.7 ± 0.2 ( p = 0.03 ) , respectively, also demonstrating that the metric introduced larger variations. Conclusions: 4D-CT pulmonary ventilation images vary widely with DIR algorithms and metrics. Careful physiologic validation to determine the appropriate DIR algorithm and metric is needed prior to its applications.
ISSN:0094-2405
2473-4209
DOI:10.1118/1.3547719