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Comparison of Different QA Methods for Deformable Image Registration to the Known Errors for Prostate and Head-And-Neck Virtual Phantoms

M Obeidat

M. Obeidat1*, G. Narayanasamy1 , K. Cline1 , S. Stathakis1 , J. Pouliot2 , H. Kim2 , and N. Kirby1 . (1) University of Texas Health Science Center at San Antonio, Cancer Therapy and Research Center, San Antonio, TX. (2) University of California San Francisco, San Francisco, CA.


SU-E-J-117 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

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 images. The resulting deformation measurement was then compared to that predicted by the algorithm. For image similarity, we calculated the root-mean-square and the mean-absolute differences between the deformed and warped undeformed images. Finally, we calculated the actual spatial registration error for each DIR algorithm, from the known deformations.

Results:The MIM algorithm produced the lowest average errors for the landmark analysis, the closest image similarity, and overall the most accurate contour transfers. When compared to the known deformations, MIM also produced the lowest average error, but the largest spatial registration errors. The lowest maximum spatial errors were made by DEF and DMP for the prostate and head-and-neck phantoms, respectively.

Conclusion:Contour comparison, landmark tracking, and image similarity all indicated that MIM was the most accurate DIR algorithm. These metrics were aligned with MIM producing the lowest known mean errors, but did not catch that MIM was also by far producing the largest known errors.

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