PURPOSE: The accuracy of deformable image registration could have a significant dosimetric impact in radiation treatment planning. Various image registration algorithms have been developed for clinical application. However, validation of these algorithms in the current clinical setting remains subjective, relying on visual assessment and lacking a comparison to the ground-truth deformation. In this study, the authors propose a framework to quantitatively validate various image registration solutions by using patient-based synthetic quality assurance (QA) phantoms, which can be applied on a site-by-site basis. METHODS: The computer-simulated deformation was first generated with virtual deformation QA software and further benchmarked using a physical pelvic phantom that was modeled after real patient CT images. After the validity of the virtual deformation was confirmed, a set of synthetic deformable images was produced to simulate various anatomical movements during radiotherapy based on real patient CT images. Three patients with head-and-neck, prostate, and spine cancer were included. The transformations included bladder filling, soft tissue deformation, mandible, and vertebral body movement, etc., which provided various ground-truth images to validate deformable registration. Several clinically available deformable registration algorithms were tested on these images with multiple registration setups, such as global or regional and single-pass or multipass optimization. The generated deformation fields and the ground-truth deformation are compared using voxel-by-voxel based analysis as well as regional based analysis. RESULTS: Performance of registration algorithms varies with clinical sites. The voxel-by-voxel analysis showed the intensity-based free-form deformation by MIM generated the greatest accuracy for low-contrast small regions that underwent significant deformation, such as bladder expansion for prostate. However, for large field deformations with strong contrast, this approach may increase errors, which is especially evident in the cranial spinal irradiation (CSI) case. Both single-pass and multipass B-spline registrations performed well for the head-and-neck patient and CSI patients. CONCLUSIONS: QA for deformable image registration is essential to verify the cumulated dose for accurate adaptive radiotherapy. In this study, the authors develop a workflow that can validate image registration techniques for several different clinical sites and for various types of deformations using patient-based simulated deformations. This work could provide a reference for clinicians and radiation physicists on how to choose appropriate image registration algorithms for different situations.
PURPOSE: The accuracy of deformable image registration could have a significant dosimetric impact in radiation treatment planning. Various image registration algorithms have been developed for clinical application. However, validation of these algorithms in the current clinical setting remains subjective, relying on visual assessment and lacking a comparison to the ground-truth deformation. In this study, the authors propose a framework to quantitatively validate various image registration solutions by using patient-based synthetic quality assurance (QA) phantoms, which can be applied on a site-by-site basis. METHODS: The computer-simulated deformation was first generated with virtual deformation QA software and further benchmarked using a physical pelvic phantom that was modeled after real patient CT images. After the validity of the virtual deformation was confirmed, a set of synthetic deformable images was produced to simulate various anatomical movements during radiotherapy based on real patient CT images. Three patients with head-and-neck, prostate, and spine cancer were included. The transformations included bladder filling, soft tissue deformation, mandible, and vertebral body movement, etc., which provided various ground-truth images to validate deformable registration. Several clinically available deformable registration algorithms were tested on these images with multiple registration setups, such as global or regional and single-pass or multipass optimization. The generated deformation fields and the ground-truth deformation are compared using voxel-by-voxel based analysis as well as regional based analysis. RESULTS: Performance of registration algorithms varies with clinical sites. The voxel-by-voxel analysis showed the intensity-based free-form deformation by MIM generated the greatest accuracy for low-contrast small regions that underwent significant deformation, such as bladder expansion for prostate. However, for large field deformations with strong contrast, this approach may increase errors, which is especially evident in the cranial spinal irradiation (CSI) case. Both single-pass and multipass B-spline registrations performed well for the head-and-neck patient and CSI patients. CONCLUSIONS: QA for deformable image registration is essential to verify the cumulated dose for accurate adaptive radiotherapy. In this study, the authors develop a workflow that can validate image registration techniques for several different clinical sites and for various types of deformations using patient-based simulated deformations. This work could provide a reference for clinicians and radiation physicists on how to choose appropriate image registration algorithms for different situations.
Authors: Abdallah S R Mohamed; Manee-Naad Ruangskul; Musaddiq J Awan; Charles A Baron; Jayashree Kalpathy-Cramer; Richard Castillo; Edward Castillo; Thomas M Guerrero; Esengul Kocak-Uzel; Jinzhong Yang; Laurence E Court; Michael E Kantor; G Brandon Gunn; Rivka R Colen; Steven J Frank; Adam S Garden; David I Rosenthal; Clifton D Fuller Journal: Radiology Date: 2014-11-07 Impact factor: 11.105