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Quantitative Evaluation of Deformable Image Registration in MRI-Guided Adaptive Radiation Therapy

K Mooney

K Mooney1*, T Zhao1 , Y Duan2 , M Zhang3 , O Green1 , S Mutic1 , D Yang1 , (1) Washington University School of Medicine, Saint Louis, MO, (2) University of Missouri, Columbia, Missouri, (3) Oregon Health and Science University, Portland, Oregon


TU-AB-202-6 (Tuesday, August 2, 2016) 7:30 AM - 9:30 AM Room: 202

Purpose: To assess the performance of the deformable image registration algorithm used for MRI-guided adaptive radiation therapy using image feature analysis.

Methods: MR images were collected from five patients treated on the MRIdian (ViewRay, Inc., Oakwood Village, OH), a three head Cobalt-60 therapy machine with an 0.35 T MR system. The images were acquired immediately prior to treatment with a uniform 1.5 mm resolution. Treatment sites were as follows: head/neck, lung, breast, stomach, and bladder. Deformable image registration was performed using the ViewRay software between the first fraction MRI and the final fraction MRI, and the DICE similarity coefficient (DSC) for the skin contours was reported. The SIFT and Harris feature detection and matching algorithms identified point features in each image separately, then found matching features in the other image. The target registration error (TRE) was defined as the vector distance between matched features on the two image sets. Each deformation was evaluated based on comparison of average TRE and DSC.

Results: Image feature analysis produced between 2000-9500 points for evaluation on the patient images. The average (± standard deviation) TRE for all patients was 3.3 mm (±3.1 mm), and the passing rate of TRE<3 mm was 60% on the images. The head/neck patient had the best average TRE (1.9 mm±2.3 mm) and the best passing rate (80%). The lung patient had the worst average TRE (4.8 mm±3.3 mm) and the worst passing rate (37.2%). DSC was not significantly correlated with either TRE (p=0.63) or passing rate (p=0.55).

Conclusions: Feature matching provides a quantitative assessment of deformable image registration, with a large number of data points for analysis. The TRE of matched features can be used to evaluate the registration of many objects throughout the volume, whereas DSC mainly provides a measure of gross overlap.

Funding Support, Disclosures, and Conflict of Interest: We have a research agreement with ViewRay Inc.

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