In this paper we apply Cosserat rod theory to catheters with permanent magnetic components that are subject to spatially varying magnetic fields. The resulting model formulation captures the magnetically coupled catheter behavior and provides numerical solutions for rod equilibrium configurations in real-time. The model is general, covering cases with different catheter geometries, multiple magnetic components, and various boundary constraints. The necessary Jacobians for quasi-static, closed-loop control using an electromagnetic coil system and a motorized advancer are derived and incorporated into a visual-feedback controller. We address the issue of solution bifurcations caused by the magnetic field by proposing an additional, stabilizing control method that makes use of system redundancies. We demonstrate the effectiveness of the model by performing 3D tip-position trajectories with root-mean-square distance errors of 2.7 mm in open-loop, 0.30 mm in closed-loop, and 0.42 mm in stabilizing closed-loop modes. The stabilizing controller achieved on average a factor of 1.6 increase in the restoring wrenches for the least stable direction.
Magnetically controlled catheters and endoscopes can improve minimally invasive procedures as a result of their increased maneuverability when combined with modern magnetic steering systems. However, such systems have two distinct shortcomings: they require continuous information about the location of the instrument inside the human body and they rely on models that accurately capture the device behavior, which are difficult to obtain in realistic settings. To address both of these issues, we propose a control algorithm that continuously estimates a magnetic endoscope’s response to changes in the actuating magnetic field. Experiments in a structured visual environment show that the control method is able to follow image-based trajectories under different initial conditions with an average control error that measures 1.8 % of the trajectory length. The usefulness for medical procedures is demonstrated with a bronchoscopic inspection task. In a proof-of-concept study, a custom 2[Formula: see text]mm diameter miniature camera endoscope is navigated through an anatomically correct lung phantom in a clinician-controlled manner. This represents the first demonstration of the controlled manipulation of a magnetic device without localization, which is critical for a wide range of medical procedures.
BackgroundBrain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients.MethodsSeven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined.ResultsfNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062).ConclusionsThis study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
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