SU‐E‐J‐117: Comparison of Different QA Methods for Deformable Image Registration to the Known Errors for Prostate and Head‐And‐Neck Virtual Phantoms

TL;DRAbstract

Purpose: Several methods can evaluate the accuracy of deformable image registration (DIR), but they are not necessarily a true measurement of DIR accuracy. The purpose here is to evaluate how these methods compare to known errors found with prostate and head‐and‐neck virtual phantoms. Methods: Quality assurance of DIR falls in three basic categories: contour comparison, landmark tracking, and image similarity. These different methods were utilized to evaluate the performance of four DIR algorithms, MIM and three from Velocity: Deformable (DEF), Deformable Multi‐Pass (DMP), and Extended Deformable Multi‐Pass (XMP). For contour comparison, organs were contoured (total of 15) on both the undeformed and deformed images. Then, the DIR algorithms were used to transfer contours from the undeformed to the deformed image and compared to that drawn directly on the deformed image. For landmark tracking, we found visible landmarks and measured their locations on the deformed and the undeformed ima

Chat with Paper

AI Agents for this Paper