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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. 

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. 

Existing fluidic soft logic gates for controlling soft robots typically depend on labor-intensive manual fabrication or costly printing methods. In our research, we utilize Fused Deposition Modeling to create fully 3D-printed fluidic logic gates, fabricating a valve from thermoplastic polyurethane. We investigate the 3D printing of tubing and introduce a novel extrusion nozzle for tubing production. Our approach significantly reduces the production time for soft fluidic valves from 27 hours using replica molding to 3 hours with FDM printing. We apply our 3D-printed valve to develop optimized XOR gates and D-latch circuits, presenting a rapid and cost- effective fabrication method for fluidic logic gates that aims to make fluidic circuitry more accessible to the soft robotics community. 

Tonsillectomy, one of the most common surgical procedures worldwide, is often associated with postoperative complications, particularly bleeding. Tonsil laser ablation has been proposed as a safer alternative; however, its adoption has been limited because it can be difficult for a surgeon to visually control the thermal interactions that occur between the laser and the tissue. In this study, we propose to monitor the ablation caused by a CO2 laser on ex-vivo tonsil tissue using photoacoustic imaging. Soft tissue’s unique photoacoustic spectra were used to distinguish between ablated and non-ablated tissue. Our results suggest that photoacoustic imaging is able to visualize necrosis formation and calculate the necrotic extent, offering the potential for improved tonsil laser ablation outcomes. 

Lasers are an essential tool in modern medical practice, and their applications span a wide spectrum of specialties. In laryngeal microsurgery, lasers are frequently used to excise tumors from the vocal folds [1]. Several research groups have recently developed robotic systems for these procedures [2-4], with the goal of providing enhanced laser aiming and cutting precision. Within this area of research, one of the problems that has received considerable attention is the automatic control of the laser focus. Briefly, laser focusing refers to the process of optically adjusting a laser beam so that it is concentrated in a small, well-defined spot – see Fig. 1. In surgical applications, tight laser focusing is desirable to maximize cutting efficiency and precision; yet, focusing can be hard to perform manually, as even slight variations (< 1 mm) in the focal distance can significantly affect the spot size. Motivated by these challenges, Kundrat and Schoob [3] recently introduced a technique to robotically maintain constant focal distance, thus enabling accurate, consistent cutting. In another study, Geraldes et al. [4] developed an automatic focus control system based on a miniaturized varifocal mirror, and they obtained spot sizes as small as 380 μm for a CO2 laser beam. Whereas previous work has mainly dealt with the problem of creating – and maintaining – small laser spots, in this paper we propose to study the utility of defocusing surgical lasers. In clinical practice, physicians defocus a laser beam whenever they wish to change its effect from cutting to heating – e.g., to thermally seal a blood vessel [5]. To the best of our knowledge, no previous work has studied the problem of robotically regulating the laser focus to achieve controlled tissue heating, which is precisely the contribution of the present manuscript. In the following sections, we first briefly review the dynamics of thermal laser-tissue interactions and then propose a controller capable of heating tissue according to a prescribed temperature profile. Laser-tissue interactions are generally considered hard to control due to the inherent inhomogeneity of biological tissue [6], which can create significant variability in its thermal response to laser irradiation. In this paper, we use methods from nonlinear control theory to synthesize a temperature controller capable of working on virtually any tissue type without any prior knowledge of its physical properties. 

This paper describes a framework allowing intraoperative photoacoustic (PA) imaging integrated into minimally invasive surgical systems. PA is an emerging imaging modality that combines the high penetration of ultrasound (US) imaging with high optical contrast. With PA imaging, a surgical robot can provide intraoperative neurovascular guidance to the operating physician, alerting them of the presence of vital substrate anatomy invisible to the naked eye, preventing complications such as hemorrhage and paralysis. Our proposed framework is designed to work with the da Vinci surgical system: real-time PA images produced by the framework are superimposed on the endoscopic video feed with an augmented reality overlay, thus enabling intuitive three-dimensional localization of critical anatomy. To evaluate the accuracy of the proposed framework, we first conducted experimental studies in a phantom with known geometry, which revealed a volumetric reconstruction error of 1.20 ± 0.71 mm. We also conducted anex vivostudy by embedding blood-filled tubes into chicken breast, demonstrating the successful real-time PA-augmented vessel visualization onto the endoscopic view. These results suggest that the proposed framework could provide anatomical and functional feedback to surgeons and it has the potential to be incorporated into robot-assisted minimally invasive surgical procedures.