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 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(voI) 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)(voI), 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.