  One of the goals of computer-aided surgery is to match intraoperative data to
preoperative images of the anatomy and add complementary information that can
facilitate the task of surgical navigation. In this context, mechanical
palpation can reveal critical anatomical features such as arteries and
cancerous lumps which are stiffer that the surrounding tissue. This work uses
position and force measurements obtained during mechanical palpation for
registration and stiffness mapping. Prior approaches, including our own,
exhaustively palpated the entire organ to achieve this goal. To overcome the
costly palpation of the entire organ, a Bayesian optimization framework is
introduced to guide the end effector to palpate stiff regions while
simultaneously updating the registration of the end effector to an a priori
geometric model of the organ, hence enabling the fusion of ntraoperative data
into the a priori model obtained through imaging. This new framework uses
Gaussian processes to model the stiffness distribution and Bayesian
optimization to direct where to sample next for maximum information gain. The
proposed method was evaluated with experimental data obtained using a Cartesian
robot interacting with a silicone organ model and an ex vivo porcine liver.
