3D Laser-and-Tissue Agnostic Data-Driven Method for Robotic Laser Surgical Planning

In robotic laser surgery, shape prediction of an one-shot ablation crater is an important problem for minimizing errant overcutting of healthy tissue during the course of pathological tissue resection and precise tumor removal. Since it is difficult to physically model the laser-tissue interaction due to the variety of optical tissue properties, complicated process of heat transfer, and uncertainty about the chemical reaction, we propose a 3D crater prediction model based on an entirely data-driven method without any assumptions of laser settings and tissue properties. Based on the crater prediction model, we formulate a novel robotic laser planning problem to determine the optimal laser incident configuration, which aims to create a crater that aligns with the surface target (e.g. tumor, pathological tissue). To solve the one-shot ablation crater prediction problem, we model the 3D geometric relation between the tissue surface and the laser energy profile as a non-linear regression problem that can be represented by a single-layer perceptron (SLP) network. The SLP network is encoded in a novel kinematic model to predict the shape of the post-ablation crater with an arbitrary laser input. To estimate the SLP network parameters, we formulate a dataset of one-shot laser-phantom craters reconstructed by the optical coherence tomography (OCT) B-scan images. To verify the method. The learned crater prediction model is applied to solve a simplified robotic laser planning problem modelled as a surface alignment error minimization problem. The initial results report about
$(91.2\pm 3.0)\%$
3D-crater-Intersection-over-Union (3D-crater-IoU) for the 3D crater prediction and an average of about 98.0% success rate for the simulated surface alignment experiments.