Optimized path planning for soft tissue resection via laser vaporization

Weston Ross; Neil Cornwell; Matthew Tucker; Brian Mann; Patrick Codd

doi:10.1117/12.2290567

8 February 2018 Optimized path planning for soft tissue resection via laser vaporization

Weston Ross, Neil Cornwell, Matthew Tucker, Brian Mann, Patrick Codd

Proceedings Volume 10480, Clinical and Translational Neurophotonics 2018; 1048006 (2018) https://doi.org/10.1117/12.2290567
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

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. The handheld laser scalpel in neurosurgery has been shown to provide a more gentle approach to tissue manipulation on or near critical structures over classical tooling, though difficulties of control have prevented large scale adoption of the tool. This paper presents a novel approach to generating a cutting path for the volumetric resection of tissue using a computer-guided laser scalpel. 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 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. We demonstrate 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.

Citation Download Citation

Weston Ross, Neil Cornwell, Matthew Tucker, Brian Mann, and Patrick Codd "Optimized path planning for soft tissue resection via laser vaporization", Proc. SPIE 10480, Clinical and Translational Neurophotonics 2018, 1048006 (8 February 2018); https://doi.org/10.1117/12.2290567

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