Needle-Guiding Robot for Laser Ablation of Liver Tumors Under MRI Guidance

Franco, Enrico; Brujić, Djordje; Rea, Marc; Gedroyc, Wladyslaw; Ristić, M.

doi:10.1109/tmech.2015.2476556

Cited by 67 publications

(72 citation statements)

References 44 publications

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“…This objective is achieved with a suitable choice of actuators and with performing control algorithms. Robotic devices for percutaneous intervention have employed either nonbackdrivable actuators (Tse et al, 2012) or compliant actuators (Iranpanah et al, 2015;Franco et al, 2016). Non-backdrivable actuators can hold their position regardless of the external forces, within their design limits.…”

Section: Methodsmentioning

confidence: 99%

“…Without loss of generality, the mass m and the area A are assumed unitary and are omitted in the rest of the paper in order to simplify the notation. Furthermore, the pressure p 0 is set constant as in Franco et al (2016) and the force p 0 a is included in the lumped disturbance d. As a result of these simplifications, the system is presented in a similar form to Karafyllis and Krstic (2013) and Monaco and Normand-Cyrot (2015). While the system (Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Mechatronics instruments for magnetic resonance imaging (MRI)-guided intervention allow conducting complex procedures that would otherwise be very time consuming and error prone (Tse et al, 2012;Yang et al, 2014;Franco et al, 2016). Typically, a specially designed robot operates inside the scanner bore where the patient cannot be easily accessed (Yang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it has been used in several robots for MRI-guided intervention (Melzer et al, 2008;Stoianovici et al, 2014). In order to minimize image degradation, several systems employ a control unit and a power source located outside the MRI room, supplying the robot through long transmission lines (Yang et al, 2011;Iranpanah et al, 2015;Franco et al, 2016). Simultaneously, the clinicians operate a master unit from the control room, where the intra-operative MR images are displayed for guidance (Franco and Ristic, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…This scenario is relevant to systems in which the controller and the power source are located remotely from the actuator, as in the case of robots for percutaneous intervention under MRI-guidance (Yang et al, 2014;Franco et al, 2016). The proposed control scheme extends the predictive controller in Karafyllis and Krstic (2013) to tracking tasks and removes the assumption of bounded disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Combined Adaptive and Predictive Control for a Teleoperation System with Force Disturbance and Input Delay

Franco

2016

Front. Robot. AI

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This work presents a new discrete-time adaptive-predictive control algorithm for a system with force disturbance and input delay. This scenario is representative of a mechatronic device for percutaneous intervention with pneumatic actuation and long supply lines which is controlled remotely in the presence of an unknown external force resulting from needle-tissue interaction or gravity. The ultimate goal of this research is the roboticassisted percutaneous intervention of the liver under Magnetic Resonance Imaging (MRI) guidance. Since the control algorithm is intended for a digital microcontroller, it is presented in the discrete-time form. The controller design is illustrated for a 1 degree-offreedom system and is conducted with a modular approach combining position control, adaptive disturbance compensation, and predictive control. The controller stability is analyzed and the effect of the input delay and of the tuning parameters is discussed. The controller performance is assessed with simulations considering a disturbance representative of needle insertion forces. The results indicate that the adaptive-predictive controller is effective in the presence of a variable disturbance and of a known or variable input delay.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combined Adaptive and Predictive Control for a Teleoperation System with Force Disturbance and Input Delay

Franco

2016

Front. Robot. AI

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Epipolar Geometry‐Based Visual Servoing of Soft Endoscopic Manipulator for Transoral Laser Ablation

Fang

Wang

et al. 2022

Advanced Intelligent Systems

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Soft manipulators integrated with optical laser fibers offer new opportunities for endoscopic noncontact laser surgery. To achieve precise laser projection in a confined workspace and avoid damage, a controller with high accuracy and stability is required. An effective way is to close the control loop. Therefore, a visual servo controller that allows automatic laser spot steering using soft manipulators is proposed. An epipolar geometry model is established to acquire the inverse transition mapping from image to actuation. With this mapping, a feedback controller is derived without prior information of tissue surface geometry. Experimental validation demonstrates accurate path following using a magnetic resonance‐safe manipulator, with root‐mean‐square (RMS) tracking errors <4 pixels (140 μm) in the camera view. This is maintained even throughout 70 repeated cycles, with a maximum tracking error <11.32 pixels (≈396.2 μm). The controller enables laser spot following of the mouse cursor with an RMS tracking error of 3.97 pixels (≈180.5 μm). Ex vivo tissue ablation tests are conducted to validate the laser ablation pattern, with an RMS tracking error of about 5.75 pixels (≈201.3 μm). A laser ablation test is conducted under magnetic resonance imaging (MRI) to validate the feasibility of MRI‐guided laser surgery.

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Simulated accuracy assessment of small footprint body‐mounted probe alignment device for MRI‐guided cryotherapy of abdominal lesions

et al. 2020

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PurposeMagnetic resonance imaging (MRI)‐guided percutaneous cryotherapy of abdominal lesions, an established procedure, uses MRI to guide and monitor the cryoablation of lesions. Methods to precisely guide cryotherapy probes with a minimum amount of trial‐and‐error are yet to be established. To aid physicians in attaining precise probe alignment without trial‐and‐error, a body‐mounted motorized cryotherapy‐probe alignment device (BMCPAD) with motion compensation was clinically tested in this study. The study also compared the contribution of body motion and organ motion compensation to the guidance accuracy of a body‐mounted probe alignment device.MethodsThe accuracy of guidance using the BMCPAD was prospectively measured during MRI‐guided percutaneous cryotherapies before insertion of the probes. Clinical parameters including patient age, types of anesthesia, depths of the target, and organ sites of target were collected. By using MR images of the target organs and fiducial markers embedded in the BMCPAD, we retrospectively simulated the guidance accuracy with body motion compensation, organ motion compensation, and no compensation. The collected data were analyzed to test the impact of motion compensation on the guidance accuracy.ResultsThirty‐seven physical guidance of probes were prospectively recorded for sixteen completed cases. The accuracy of physical guidance using the BMCPAD was 13.4 ± 11.1 mm. The simulated accuracy of guidance with body motion compensation, organ motion compensation, and no compensation was 2.4 ± 2.9 mm, 2.2 ± 1.6 mm, and 3.5 ± 2.9 mm, respectively. Data analysis revealed that the body motion compensation and organ motion compensation individually impacted the improvement in the accuracy of simulated guidance. Moreover, the difference in the accuracy of guidance either by body motion compensation or organ motion compensation was not statistically significant. The major clinical parameters impacting the accuracy of guidance were the body and organ motions. Patient age, types of anesthesia, depths of the target, and organ sites of target did not influence the accuracy of guidance using BMCPAD. The magnitude of body surface movement and organ movement exhibited mutual statistical correlation.ConclusionsThe BMCPAD demonstrated guidance accuracy comparable to that of previously reported devices for CT‐guided procedures. The analysis using simulated motion compensation revealed that body motion compensation and organ motion compensation individually impact the improvement in the accuracy of device‐guided cryotherapy probe alignment. Considering the correlation between body and organ movements, we also determined that body motion compensation using the ring fiducial markers in the BMCPAD can be solely used to address both body and organ motions in MRI‐guided cryotherapy.

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Needle-Guiding Robot for Laser Ablation of Liver Tumors Under MRI Guidance

Cited by 67 publications

References 44 publications

Combined Adaptive and Predictive Control for a Teleoperation System with Force Disturbance and Input Delay

Combined Adaptive and Predictive Control for a Teleoperation System with Force Disturbance and Input Delay

Epipolar Geometry‐Based Visual Servoing of Soft Endoscopic Manipulator for Transoral Laser Ablation

Simulated accuracy assessment of small footprint body‐mounted probe alignment device for MRI‐guided cryotherapy of abdominal lesions

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