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This paper reports on a study whose goal is to control the tissue temperature at a specific spot during laser surgery, for the purpose of, e.g., inducing coagulation or sealing blood vessels. We propose a solution that relies on the automatic adjustment of the laser focus (and thus how concentrated the laser beam is), combined with the use of an infrared thermal camera for non-contact temperature monitoring. One of the main challenges in the control of thermal laser-tissue interactions is that these interactions can be hard to predict due to the inherent variability in the molecular composition of biological tissue. To tackle this challenge, we explore two different control approaches: (1) a model-less controller using a Proportional- Integral (PI) formulation, whose gains are set via a tuning procedure performed on laboratory-made tissue phantoms; and (2) a model-based controller using an adaptive formulation that makes it robust to tissue variability. We report on experiments, performed on four types of tissue specimens, showing that both controllers can consistently achieve temperature tracking with a Root-Mean-Square Error (RMSE) ≈ 1 ◦C.
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This content will become publicly available on May 14, 2026
Towards a Physics Engine to Simulate Robotic Laser Surgery: Finite Element Modeling of Thermal Laser-Tissue Interactions
This paper presents a computational model, based on the Finite Element Method (FEM), that simulates the thermal response of laser-irradiated tissue. This model addresses a gap in the current ecosystem of surgical robot simulators, which generally lack support for lasers and other energy-based end effectors. In the proposed model, the thermal dynamics of the tissue are calculated as the solution to a heat conduction problem with appropriate boundary conditions. The FEM formulation allows the model to capture complex phenomena, such as convection, which is crucial for creating realistic simulations. The accuracy of the model was verified via benchtop laser-tissue interaction experiments using agar tissue phantoms and ex-vivo chicken muscle. The results revealed an average root-meansquare error (RMSE) of less than 2 ◦C across most experimental conditions.
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- Award ID(s):
- 2237011
- PAR ID:
- 10608065
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-9900-3
- Page Range / eLocation ID:
- 129 to 135
- Format(s):
- Medium: X
- Location:
- Atlanta, GA, USA
- Sponsoring Org:
- National Science Foundation
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