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Registration uncertainty in deforming organs: a novel approach for ensuring navigational confidence during image-guided procedures

Successful estimation of target registration error (TRE) would provide immense opportunities for controlling risks associated with navigation during image-guided surgery. While developed theories exist for predicting spatial distributions of TRE for rigid point-based registration, similar capabilities in the domain of deformable registration are still needed to develop truly reliable image guidance systems for navigation in soft tissue organs. Recently, breakthrough work derived two analytic uncertainty metrics based on the dissipation of constraint energy over distance to measure the susceptibility of elastic deformable registration to errors originating from unknown effects that occur where registration constraints are missing. In this work, these registration uncertainties are leveraged to classify error thresholds for detecting spatial regions that become vulnerable to inaccuracy in sparse data driven elastic registrations. With a large dataset of over 6000 registrations, receiver operating characteristic (ROC) analysis was performed to assess discriminatory performance of these uncertainty metrics to clinically relevant levels of registration error and to identify optimal binary cutoffs for their prediction. Both uncertainty metrics were capable of detecting regions of the organ where deformable registration accuracy exceeded the average magnitude of rigid registration error with AUC above 0.87. Furthermore, both metrics detected regions of the organ with TRE greater than 10 mm with AUC of approximately 0.8. These new capabilities will enhance clinical confidence in image-guided technologies in deforming organs through enabling immediate quantification and communication of navigational reliability and system accuracy during soft tissue surgery.