Technical note: Intensity‐based quality assurance criteria for deformable image registration in image‐guided radiotherapy

Abstract

BackgroundDeformable image registration is increasingly used in radiotherapy to adapt the treatment plan and accumulate the delivered dose. Consequently, clinical workflows using deformable image registration require quick and reliable quality assurance to accept registrations. Additionally, for online adaptive radiotherapy, quality assurance without the need for an operator to delineate contours while the patient is on the treatment table is needed. Established quality assurance criteria such as the Dice similarity coefficient or Hausdorff distance lack these qualities and also display a limited sensitivity to registration errors beyond soft tissue boundaries.PurposeThe purpose of this study is to investigate the existing intensity‐based quality assurance criteria structural similarity and normalized mutual information for their ability to quickly and reliably identify registration errors for (online) adaptive radiotherapy and compare them to contour‐based quality assurance criteria.MethodsAll criteria were tested using synthetic and simulated biomechanical deformations of 3D MR images as well as manually annotated 4D CT data. The quality assurance criteria were scored for classification performance, for their ability to predict the registration error, and for their spatial information.ResultsWe found that besides being fast and operator‐independent, the intensity‐based criteria have the highest area under the receiver operating characteristic curve and provide the best input for models to predict the registration error on all data sets. Structural similarity furthermore provides spatial information with a higher gamma pass rate of the predicted registration error than commonly used spatial quality assurance criteria.ConclusionsIntensity‐based quality assurance criteria can provide the required confidence in decisions about using mono‐modal registrations in clinical workflows. They thereby enable automated quality assurance for deformable image registration in adaptive radiotherapy treatments.

Experimental validation of multi-fraction online adaptations in magnetic resonance guided radiotherapy

“…Therefore, patient-specific QA is often performed on individual pre-treatment or online adapted fractions instead of the total series of online adaptive fractions [5] . Deformable image registration (DIR) has been used to accumulate the dose of online adapted plans using dose warping [10] , [11] , [12] , however this approach introduces an uncertainty in the accumulated dose and difficulties verifying correctness of DIR [13] , [14] , [15] .…”