In robotic laser surgery, shape prediction of an one-shot ablation cavity 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 cavity prediction model based on an entirely data-driven method without any assumptions of laser settings and tissue properties. Based on the cavity prediction model, we formulate a novel robotic laser planning problem to determine the optimal laser incident configuration, which aims to create a cavity that aligns with the surface target (e.g. tumor, pathological tissue). To solve the one-shot ablation cavity 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 cavity with an arbitrary laser input. To estimate the SLP network parameters, we formulate a dataset of one-shot laser-phantom cavities reconstructed by the optical coherence tomography (OCT) B-scan images for the data-driven modelling. To verify the method. The learned cavity prediction model is applied to solve a simplified robotic laser planning problem modelled as a surface alignment error minimization problem. The initial results report (91.1 +- 3.0)% 3D-cavity-Intersection-over-Union (3D-cavity-IoU) for the 3D cavity prediction and an average of 97.9% success rate for the simulated surface alignment experiments.

翻译：在机器人激光手术中，单次烧蚀空腔的形状预测对于减少病理组织切除和精准肿瘤移除过程中对健康组织的误切至关重要。由于激光与组织相互作用的物理建模受制于组织光学特性的多样性、传热过程的复杂性以及化学反应的不确定性，我们提出了一种完全基于数据驱动的3D空腔预测模型，该模型无需对激光参数和组织特性进行任何假设。基于该空腔预测模型，我们构建了一个新颖的机器人激光规划问题，用于确定最优激光入射配置，旨在生成与表面目标（如肿瘤、病理组织）对齐的空腔。为解决单次烧蚀空腔预测问题，我们将组织表面与激光能量分布之间的3D几何关系建模为非线性回归问题，该问题可由单层感知机网络表示。该SLP网络被编码至一个新颖的运动学模型中，用于预测任意激光输入下的烧蚀后空腔形状。为估计SLP网络参数，我们构建了由光学相干断层扫描B-scan图像重建的单次激光-仿体空腔数据集以进行数据驱动建模。为验证该方法，将学习得到的空腔预测模型应用于简化的机器人激光规划问题，该问题被建模为表面对齐误差最小化问题。初步结果显示，3D空腔预测的3D空腔交并比达到(91.1 ± 3.0)%，模拟表面对齐实验的平均成功率为97.9%。