Purpose: Introducing computational methods to laser surgery are an emerging field. Focusing on endoscopic laser interventions, a novel approach is presented to enhance intraoperative incision planning and laser focusing by means of tissue surface information obtained by stereoscopic vision.
Methods: Tissue surface is estimated with stereo-based methods using nonparametric image transforms. Subsequently, laser-to-camera registration is obtained by ablating a pattern on tissue substitutes and performing a principle component analysis for precise laser axis estimation. Furthermore, a virtual laser view is computed utilizing trifocal transfer. Depth-based laser focus adaptation is integrated into a custom experimental laser setup in order to achieve optimal ablation morphology. Experimental validation is conducted on tissue substitutes and ex vivo animal tissue.
Results: Laser-to-camera registration gives an error between planning and ablation of less than 0.2 mm. As a result, the laser workspace can accurately be highlighted within the live views and incision planning can directly be performed. Experiments related to laser focus adaptation demonstrate that ablation geometry can be kept almost uniform within a depth range of 7.9 mm, whereas cutting quality significantly decreases when the laser is defocused.
Conclusions: An automatic laser focus adjustment on tissue surfaces based on stereoscopic scene information is feasible and has the potential to become an effective methodology for optimal ablation. Laser-to-camera registration facilitates advanced surgical planning for prospective user interfaces and augmented reality extensions.