使用贝叶斯优化指导同时配准和刚度映射的柔性环境探测
Using Bayesian Optimization to Guide Probing of a Flexible Environment for Simultaneous Registration and Stiffness Mapping
摘要：
计算机辅助手术的目标之一是将术中数据与解剖结构的术前图像相匹配，并添加可以促进手术导航任务的补充信息。在这种情况下，机械触诊可以揭示关键的解剖学特征，如动脉和癌性肿块，它们比周围组织更硬。这项工作使用的位置和力的测量过程中获得的机械触诊登记和刚度映射。以前的方法，包括我们自己的方法，都是彻底触诊整个器官来实现这一目标。为了克服整个器官的昂贵触诊，引入贝叶斯优化框架来引导末端执行器触诊刚性区域，同时更新末端执行器到器官的先验几何模型的配准，因此使得能够将术中数据融合到通过成像获得的先验模型中。这个新的框架使用高斯过程来模拟刚度分布和贝叶斯优化来指导下一次采样的位置，以获得最大的信息增益。所提出的方法进行了评估，使用笛卡尔机器人与硅胶器官模型和离体猪肝相互作用获得的实验数据。
英文摘要：
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.
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