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One of the goals of computer-aided surgery is to match intraoperative data topreoperative images of the anatomy and add complementary information that canfacilitate the task of surgical navigation. In this context, mechanicalpalpation can reveal critical anatomical features such as arteries andcancerous lumps which are stiffer that the surrounding tissue. This work usesposition and force measurements obtained during mechanical palpation forregistration and stiffness mapping. Prior approaches, including our own,exhaustively palpated the entire organ to achieve this goal. To overcome thecostly palpation of the entire organ, a Bayesian optimization framework isintroduced to guide the end effector to palpate stiff regions whilesimultaneously updating the registration of the end effector to an a priorigeometric model of the organ, hence enabling the fusion of ntraoperative datainto the a priori model obtained through imaging. This new framework usesGaussian processes to model the stiffness distribution and Bayesianoptimization to direct where to sample next for maximum information gain. Theproposed method was evaluated with experimental data obtained using a Cartesianrobot interacting with a silicone organ model and an ex vivo porcine liver.

Using Bayesian Optimization to Guide Probing of a Flexible Environment for Simultaneous Registration and Stiffness Mapping