Abstract
The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping. The authors describe a DIR based adaptive radiotherapy workflow, using CT and cone-beam CT (CBCT) imaging. The transformations that mapped the anatomy between the two time points were obtained using four different DIR approaches available in NiftyReg. These included a standard unidirectional algorithm and more sophisticated bidirectional ones that encourage or ensure inverse consistency. The forward (CT-to-CBCT) deformation vector fields (DVFs) were used to propagate the CT Hounsfield units and structures to the daily geometry for "dose of the day" calculations, while the backward (CBCT-to-CT) DVFs were used to remap the dose of the day onto the planning CT (pCT). Data from five head and neck patients were used to evaluate the performance of each implementation based on geometrical matching, physical properties of the DVFs, and similarity between warped dose distributions. Geometrical matching was verified in terms of dice similarity coefficient (DSC), distance transform, false positives, and false negatives. The physical properties of the DVFs were assessed calculating the harmonic energy, determinant of the Jacobian, and inverse consistency error of the transformations. Dose distributions were displayed on the pCT dose space and compared using dose difference (DD), distance to dose difference, and dose volume histograms. All the DIR algorithms gave similar results in terms of geometrical matching, with an average DSC of 0.85 ± 0.08, but the underlying properties of the DVFs varied in terms of smoothness and inverse consistency. When comparing the doses warped by different algorithms, we found a root mean square DD of 1.9% ± 0.8% of the prescribed dose (pD) and that an average of 9% ± 4% of voxels within the treated volume failed a 2%pD DD-test (DD2%-pp). Larger DD2%-pp was found within the high dose gradient (21% ± 6%) and regions where the CBCT quality was poorer (28% ± 9%). The differences when estimating the mean and maximum dose delivered to organs-at-risk were up to 2.0%pD and 2.8%pD, respectively. The authors evaluated several DIR algorithms for CT-to-CBCT registrations. In spite of all methods resulting in comparable geometrical matching, the choice of DIR implementation leads to uncertainties in dose warped, particularly in regions of high gradient and/or poor imaging quality.
Highlights
Deformable image registration (DIR) is an active and important area of research with multiple applications in adaptive radiotherapy (ART), for head and neck (HN) patients.1,2 The planning CT can be deformed to match the daily anatomy to calculate the dose delivered per fraction,3,4 the deformations can be applied to propagate contours,5,6 and the fraction by fraction dose maps can be warped back to a common reference frame for summation.7,8In image registration at least two input images are necessary, the source and the target images, and the result is a transformation (Ts→ t) that can be used to deform the source image (s) onto the target image (t)
When comparing the doses warped by different algorithms, we found a root mean square dose difference (DD) of 1.9% ± 0.8% of the prescribed dose and that an average of 9% ± 4% of voxels within the treated volume failed a 2%pD DD-test (DD2%-pp)
We found that DIRics had difficulty in recovering larger and complex deformations, while DIRsvf performed well in the alignment of soft tissues
Summary
Deformable image registration (DIR) is an active and important area of research with multiple applications in adaptive radiotherapy (ART), for head and neck (HN) patients. The planning CT (pCT) can be deformed to match the daily anatomy to calculate the dose delivered per fraction, the deformations can be applied to propagate contours, and the fraction by fraction dose maps can be warped back to a common reference frame for summation.7,8In image registration at least two input images are necessary, the source and the target images, and the result is a transformation (Ts→ t) that can be used to deform the source image (s) onto the target image (t). The majority of the research and commercial registration algorithms are unidirectional, which means they only optimize the forward transformation (Ts→ t) and do not consider the transformation in the opposite direction (Tt→ s), which can be used to deform the target image onto the source image. Symmetric registration means that identical transformations are obtained when the roles of source and target images are switched: if the source image becomes the target (t′, such that t′ = s) and the target is the source (s′, such that s′ = t), a symmetric algorithm will ensure that Tt′→ s′ = Ts→ t and Ts′→ t′ = Tt→ s. If Tt→ s is obtained by numerically estimating the inverse of the unidirectional registration result (Ts→ t), the transformations are inverse consistent but are not symmetric
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
