2D-3D Registration Accuracy Estimation for Optimised Planning of Image-Guided Pancreatobiliary Interventions

We describe a fast analytical method to estimate landmark-based 2D-3D registration accuracy to aid the planning of pancreatobiliary interventions in which ERCP images are combined with information from diagnostic 3D MR or CT images. The method analytically estimates a target registration error (TRE), accounting for errors in the manual selection of both 2D- and 3D landmarks, that agrees with Monte Carlo simulation to within 4.5 ± 3.6 % (mean ± SD). We also show how to analytically estimate a planning uncertainty incorporating uncertainty in patient positioning, and utilise it to support ERCP-guided procedure planning by selecting the optimal patient position and X-ray C-arm orientation that minimises the expected TRE. Simulated- and derived planning uncertainties agreed to within 17.9 ± 9.7 % when the root-mean-square error was less than 50°. We demonstrate the feasibility of this approach on clinical data from two patients

Measuring and Modeling Soft Tissue Deformation for Image Guided Interventions

This paper outlines the limitations of the rigid body assumption in image guided interventions and describes how intra-operative imaging provides a rich source of information on spatial location of key structures allowing a preoperative plan to be updated during an intervention. Soft tissue deformation and variation from an atlas to a particular individual can both be determined using non-rigid registration. Classic methods using free-form deformations have a very large number of degrees of freedom. Three examples - motion models, biomechanical models and statistical shape models - are used to illustrate how prior information can be used to restrict the number of degrees of freedom of the registration algorithm to produce solutions that could plausibly be used to guide interventions. We provide preliminary results from applications in each. © Springer-Verlag Berlin Heidelberg 2003