Quantitative Evaluation of a Cone Beam Computed Tomography (CBCT)-CT Deformable Image Registration Method for Adaptive Radiation Therapy

Abstract

Purpose: Deformable or non-rigid registration is an essential tool in both Adaptive Radiation Therapy (ART) and Image Guided Radiation Therapy (IGRT) to account for soft tissue changes during the course of radiation therapy. The most common evaluation method used to assess the accuracy of deformable image registration is qualitative human evaluation. We propose a methodology to systematically measure the accuracy of an algorithm in recovering artificially introduced deformations in cases of rigid geometry. The method is entirely computer-driven and eliminates biased interpretation associated with human evaluation.

Materials and Methods: Our method involves using planning computed tomography (PCT) images acquired via conventional CT simulator as well as cone-beam CT (CBCT) acquired daily via LINAC-mounted kilovoltage image system in the treatment delivery room. The deformation occurring between the PCT and daily CBCT images was obtained using a modified version of the B-Spline deformable model designed to overcome the low soft tissue contrast and artifacts/distortions observed in the CBCT images. Clinical CBCT images and contours of phantom and central nervous system (CNS) cases were deformed (or warped) with known random deformation. In registering the deformed with the non-deformed image sets, we tracked the algorithm?s ability to recover the original, non-deformed set. Registration error was measured as the mean and maximum difference between the original and the registered surface contours from outlined structures. Using this approach, two sets of tests can be devised. To measure the residual error related to the optimizer?s convergence performance, the warped CT image is registered to the unwarped version of itself, eliminating unknown factors such as noise and positioning errors. To study additional errors introduced by artifacts and noise in the CBCT, the warped CBCT is registered to the original CT.

Results: Mean residual error introduced by the algorithm?s performance on noise-free images was less than 1 mm, with a maximum of 2 mm. The deformable image registration model was capable of accommodating significant variability of structures over time, as the artificially introduced deformation magnitude did not significantly influence the residual error. On the second type of tests, noise and artifacts decreased registration accuracy to a mean / maximum of 1.33 and 4.86 mm.

Conclusion: Deformable image registration accuracy can be easily and consistently measured by evaluating the algorithm?s ability to recover artificially introduced deformations in rigid cases, where the true solution is known apriori. The method is completely automated and does not require any user interaction.