Utility and validation of biomechanical deformable image registration in low-contrast images.

PURPOSE: The application of a biomechanical deformable image registration algorithm has been demonstrated to overcome the potential limitations in the use of intensity-based algorithms on low-contrast images that lack prominent features. Because validation of deformable registration is particularly challenging on such images, the dose distribution predicted via a biomechanical algorithm was evaluated using the measured dose from a deformable dosimeter. METHODS AND MATERIALS: A biomechanical model-based image registration algorithm registered computed tomographic (CT) images of an elastic radiochromic dosimeter between its undeformed and deformed positions. The algorithm aligns the external boundaries of the dosimeter, created from CT contours, and the internal displacements are solved by modeling the physical material properties of the dosimeter. The dosimeter was planned and irradiated in its deformed position, and subsequently, the delivered dose was measured with optical CT in the undeformed position. The predicted dose distribution, created by applying the deformable registration displacement map to the planned distribution, was then compared with the measured optical CT distribution. RESULTS: Compared with the optical CT distribution, biomechanical image registration predicted the position and size of the deformed dose fields with mean errors of ≤1 mm (maximum, 3 mm). The accuracy did not differ between cross sections with a greater or lesser deformation magnitude despite the homogenous CT intensities throughout the dosimeter. The overall 3-dimensional voxel passing rate of the predicted distribution was γ3%/3mm = 91% compared with optical CT. CONCLUSIONS: Biomechanical registration accurately predicted the deformed dose distribution measured in a deformable dosimeter, whereas previously, evaluations of a commercial intensity-based algorithm demonstrated substantial errors. The addition of biomechanical algorithms to the collection of adaptive radiation therapy tools would be valuable for dose accumulation, particularly in feature-poor images such as cone beam CT and organs such as the liver.

Duke Scholars

Author Mark Oldham Radiation Oncology