Purpose-This paper presents the development of a body-mounted robotic assistant for magnetic resonance imaging (MRI)-guided low back pain injection. Our goal was to eliminate the radiation exposure of traditional X-ray guided procedures while enabling the exquisite image quality available under MRI. The robot is designed with a compact and lightweight profile that can be mounted directly on the patient's lower back via straps, thus minimizing the effect of patient motion by moving along with the patient. The robot was built with MR-Conditional materials and actuated with piezoelectric motors so it can operate inside the MRI scanner bore during imaging and therefore streamline the clinical workflow by utilizing intraoperative MR images. Methods-The robot is designed with a four degrees-of-freedom (DOF) parallel mechanism, stacking two identical Cartesian stages, to align the needle under intraoperative MRI-guidance. The system targeting accuracy was first evaluated in free space with an optical tracking system, and further assessed with a phantom study under live MRI-guidance. Qualitative imaging quality evaluation was performed on a human volunteer to assess the image quality degradation caused by the robotic assistant. Results-Free space positioning accuracy study demonstrated that the mean error of the tip position to be 0.51 ± 0.27mm and needle angle to be 0.70 ± 0.38°. MRI-guided phantom study indicated the mean errors of the target to be 1.70±0.21mm, entry point to be 1.53 ± 0.19mm, and needle angle to be 0.66 ±0.43°. Qualitative imaging quality evaluation validated that the image degradation caused by the robotic assistant in the lumbar spine anatomy is negligible. Conclusions-The study demonstrates that the proposed body-mounted robotic system is able to perform MRI-guided low back injection in a phantom study with sufficient accuracy and with minimal visible image degradation that should not affect the procedure.

Catheters are increasingly being used to tackle problems in the cardiovascular system. However, positioning precision of the catheter tip is negatively affected by hysteresis. To ensure tissue damage due to imprecise positioning is avoided, hysteresis is to be understood and compensated for. This work investigates the feasibility to model hysteresis with a Long Short-Term Memory (LSTM) network. A bench-top setup containing a catheter distal segment was developed for model evaluation. The LSTM was first tested using four groups of test datasets containing diverse patterns. To compare with the LSTM, a Deadband Rate-Dependent Prandtl-Ishlinskii (DRDPI) model and a Support Vector Regression (SVR) model were established. The results demonstrated that the LSTM is capable of predicting the tip bending angle with sub-degree precision. The LSTM outperformed the DRDPI model and the SVR model by 60.1% and 36.0%, respectively, in arbitrarily varying signals. Next, the LSTM was further validated in a 3D reconstruction experiment using Forward-Looking Optical Coherence Tomography (FL-OCT). The results revealed that the LSTM was able to accurately reconstruct the environment with a reconstruction error below 0.25 mm. Overall, the proposed LSTM enabled precise free-space control of a robotic catheter in the presence of severe hysteresis. The LSTM predicted the catheter tip response precisely based on proximal input pressure, minimizing the need to install sensors at the catheter tip for localization.

Endovascular catheterization is an intervention which offers a low risk alternative to open surgery in many patients.Today's interventions rely heavily on fluoroscopic imaging to guide interventionalists. Fluoroscopy only produces 2D visualization of the catheter and also exposes both the patient and interventionalists to harmful radiation. Different approaches have been proposed to overcome the limitations of fluoroscopy. Fiber Bragg Grating (FBG)-based shape sensing is becoming popular to reconstruct the catheter shape. Multi-core fibers with parallel optical cores are interesting as they allow 3D shape reconstruction with a single fiber. A common issue with FBG-based shape sensing is its sensitivity to variations in twist. Even small amounts of twist can significantly impact the overall shape reconstruction accuracy. This work proposes a novel approach which combines electromagnetic tracking (EMT), FBG-based shape sensing, and sparse fluoroscopic images. The method provides realtime 3D visualization of the catheter without the need of continuous fluoroscopy. A unique feature of the proposed method is the selective use of imaging for dynamic twist-compensation of the FBG sensor. The proposed sensor-fusion method improved 3D reconstruction accuracy. Real-world in-vitro experiments promising results. For a catheter with an embedded fiber length of 170 mm, the proposed approach the 3D shape with a median root-mean-square (rms) error of 0.39 mm and an interquartile range of 0.10 mm in the 2D experiment in which the catheter was bent in a plane. A median rms error of 0.54 mm and an interquartile range of 0.07 mm were achieved in the 3D experiments.

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