Robot-guided laser ablation for surgical applications potentially offers many advantages compared to by-hand mechanical tissue cutting. However, given that tissue can be rough and/or uneven, ablation quality can be compromised if the beam waist deviates significantly from the target tissue surface. Therefore, we present a method that uses optical coherence tomography (OCT) for dynamic refocusing of robot-guided surgical laser ablation. A 7-DOF robotic manipulator with an OCT-equipped optical payload was used to simulate robotic guided laser osteotomy. M-mode OCT feedback is used for continuous surface detection to correct for axial deviations along the ablation path due to surface nonuniformity. We were able to show that such a correction scheme could maintain the beam waist within the depth of focus for surface variation as aggressive as 45 deg with feed rates up to 1 mm∕s. Strategies for implementation in surgical and nonsurgical applications are examined.
Laser ablation of bone for the purposes of osteotomy is not as well understood as ablation of homogeneous, non-biological materials such as metals and plastics. Ignition times and etch rate can vary during ablation of cortical bone. In this study, we propose the use of two techniques to optimize bone ablation at 1064nm using a coaxial nitrogen jet as an assist gas and topical application of graphite as a highly absorbing chromophore. We show a two order of magnitude reduction in mean time to ignition and variance by using the graphite topical chromophore. We also show that an increase in volumetric flow rate of the assist gas jet does show an initial increase in etch rate, but increased pressure beyond a certain point shows decreased return. This study also demonstrates a 2 nd order relationship between exposure time, volumetric flow rate of nitrogen, and etch rate of cortical bone. The results of this study can be used to optimize the performance of laser ablation systems for osteotomy. This is a companion study to an earlier one carried out by Wong et al. [Biomedical Opt. Express 6, 1 (2015)].
In this paper, we present a novel method to estimate chemical reaction and diffusion rates for biochemical reaction–diffusion dynamics from a time series of observations. Our approach leverages iterated particle filtering as a means to fit a high-dimensional stochastic and discrete spatiotemporal model to sparse time series data, often with some chemical species present in low copy numbers. We demonstrate the feasibility of this approach on three realistic reaction–diffusion systems. In each case, the method recovered known true values for all rate parameters with a great degree of accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.