Adaptive Control of Volumetric Laser Photoblation Surgery

Laser scalpels are utilized across a variety of invasive and non-invasive surgical procedures due to their precision and non-contact nature. Meanwhile, robotic and robotic-assisted surgeries are becoming more prevalent with the promise of improving surgical outcomes through increased precision, reduced operating times, and minimally invasive procedures. This dissertation presents methods and devices developed to enable assistive robotic laser surgery, with the goal of realizing the surgical benefits of both and ultimately improving surgical outcomes for patients.

The device is first used to demonstrate targeted soft tissue resection in porcine brain in an open-loop fashion. This device, coined the "TumorCNC" combines 3D scanning capabilities with a steerable surgical laser. Results show high variance around target cut depths which motivats the need for a closed-loop feedback and control as well as characterization of laser-tissue interactions for predictive modeling.

To begin to address the technical difficulties of closed-loop ablation, a model-based approach is taken. A soft tissue ablation simulator is developed and used in conjunction with an optimization routine to select parameters which maximize the total resection of target tissue while minimizing the damage to surrounding tissue. The optimization is performed using genetic algorithms. The simulator predicts the ablative properties of tissue from an interrogation cut for tuning and simulates the removal of a tumorous tissue embedded on the surface of healthy tissue using a laser scalpel. This demonstrates the ability to control depth and smoothness of cut using genetic algorithms to optimize the ablation parameters and cutting path. The laser power level, cutting rate and spacing between cuts are optimized over multiple surface cuts to achieve the desired resection volumes.

Noting that the modeling approached developed is applicable to other laser treatments requiring uniformity of laser energy deposition, a study of superficial region ablation is performed for applications in dermatology. The TumorCNC is now outfitted with an RGB-D camera. To accurately ablate targets chosen from the color image, a 3D extrinsic calibration method between the RGB-D camera frame and the laser coordinate system is implemented. The accuracy of the calibration method is tested on phantoms with planar and cylindrical surfaces. Positive error and negative error, as defined as undershooting and overshooting over the target area, are reported for each test. For 60 total test cases, the root-mean-square of the positive and negative error in both planar and cylindrical phantoms is less than 1.0mm, with a maximum absolute error less than 2.0mm. This work demonstrates the feasibility of automated laser therapy with surgeon oversight via our sensor system.

As a demonstration of the culmination of these techniques, a closed-loop, adaptive online estimation of ablative properties for soft tissue laser resection of tumors is demonstrated. First, a laser photoablation feature is created in an agarose based tissue phantom using a robotic laser photoablation device equipped with a carbon dioxide laser. Second, the device measures the surface profile of the ablated feature for analysis. Genetic algorithms in conjunction with the photoablation simulator based on the steady-state photoablation model are used to estimate the photoablation enthalpy, density, and ablative radiant threshold of the tissue phantom. The parameters and model are validated through comparison of predicted and measured surface ablations at varying depths. This approach proved effective for predicting the resulting surface profiles for small cut depths (<= 2mm) and generating laser cut paths to reach a desired depth of cut for a large surface area. This work is enabling of closed-loop resection of tissue in robotic laser surgery.