No abstract

Laser surgery has gained clinical importance due to numerous advantages including contact-free processing, arbitrary cutting geometries, and high precision. However, online process control remains a challenge for widespread clinical use. Therefore, we established a combined setup of a pulsed Er:YAG laser ( = 2940 nm) and an optical coherence tomography (OCT) ( = 930 nm) for in situ monitoring of hard tissue ablation. The optical setup facilitates an interactive control of the laser ablation depth and remaining tissue strength through the depth resolution of OCT. The 3D OCT data-set, which is acquired after ablation, provides contours and layer thicknesses.

We present a novel one-step calibration methodology for geometrical distortion correction for optical coherence tomography (OCT). A calibration standard especially designed for OCT is introduced, which consists of an array of inverse pyramidal structures. The use of multiple landmarks situated on four different height levels on the pyramids allow performing a 3D geometrical calibration. The calibration procedure itself is based on a parametric model of the OCT beam propagation. It is validated by experimental results and enables the reduction of systematic errors by more than one order of magnitude. In future, our results can improve OCT image reconstruction and interpretation for medical applications such as real time monitoring of surgery.

In recent years, optical coherence tomography (OCT) has gained increasing attention not only as an imaging device, but also as a guidance system for surgical interventions. In this contribution, we propose OCT as an external high-accuracy guidance system, and present an experimental setup of an OCT combined with a cutting laser. This setup enables not only in situ monitoring, but also automatic, highaccuracy, three-dimensional navigation and processing. Its applicability is evaluated simulating a robotic assisted surgical intervention, including planning, navigation, and processing. First results demonstrate that OCT is suitable as a guidance system, fulfilling accuracy demands of interventions such as the cochlear implant surgery.

Designing a Kalman filter requires knowledge about the stochastic part of the system. Thus, disturbances affecting states and measurements should be known. However, in practical application these disturbances are usually unknown. In this contribution a modification of the autocovariance least-square method is presented. This method converts the measurement and process noise covariance estimation problem into a least squares functional, which can be solved with a Landweber iteration to regularize the illposed problem. Then, a tuned Kalman filter gain can be calculated. A simulative evaluation is introduced to prove the method regarding robustness against modeling error and variance of the estimates.