Graeme P. Penney

Tracking liver motion using 3-D ultrasound and a surface based statistical shape model

We present a technique for registering information from preoperative CT or MR images to physical space using intraoperatively acquired 3-D ultrasound data and a surface-based statistical shape model. The model is subject-specific and captures the statistical modes of variation of the liver surface through the breathing cycle. The registration uses a Bayesian formulation, which enables information about the likely position in the breathing cycle to be incorporated in the form of prior knowledge. It is computed using the model and the ultrasound image intensities, and is constrained by the model to produce realistic surfaces. Once an initial registration is computed, the liver motion and deformation can be tracked using a single ultrasound image combined with the statistical model. The technique is demonstrated by registering models constructed for 3 different volunteers to ultrasound data acquired at different points in the breathing cycle. This method has potential application in treatment of any abdominal organ which is affected by breathing motion.

Template selection and rejection for robust nonrigid 3D registration in the presence of large deformations

The purpose of the proposed template propagation method is to support the comparative analysis of image pairs even when large deformations (e.g. from movement) are present. Starting from a position where valid starting estimates are known, small sub-volumes (templates) are registered rigidly. Propagating registration results to neighboring templates, the algorithm proceeds layer by layer until corresponding points for the whole volume are available. Template classification is important for defining the templates to be registered, for propagating registration results and for selecting successfully registered templates which finally represent the motion vector field. This contribution discusses a template selection and classification strategy based on the analysis of the similarity measure in the vicinity of the optimum. For testing the template propagation and classification methods, deformation fields of four volume pairs exhibiting considerable deformations have been estimated and the results have been compared to corresponding points picked by an expert. In all four cases, the proposed classification scheme was successful. Based on homologous points resulting from template propagation, an elastic transformation was performed.

Estimating Sparse Deformation Fields Using Multiscale Bayesian Priors and 3-D Ultrasound

This paper presents an extension to the standard Bayesian image analysis paradigm to explicitly incorporate a multiscale approach. This new technique is demonstrated by applying it to the problem of compensating for soft tissue deformation of pre-segmented surfaces for image-guided surgery using 3-D ultrasound. The solution is regularised using knowledge of the mean and Gaussian curvatures of the surface estimate. Results are presented from testing the method on ultrasound data acquired from a volunteers liver. Two structures were segmented from an MR scan of the volunteer: the liver surface and the portal vein. Accurate estimates of the deformed surfaces were successfully computed using the algorithm, based on prior probabilities defined using a minimal amount of human intervention. With a more accurate prior model, this technique has the possibility to completely automate the process of compensating for intraoperative deformation in image-guided surgery.