A Direct Drive Parallel Plane Piezoelectric Needle Positioning Robot for MRI Guided Intraspinal Injection

Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI

PURPOSE

Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset.

METHODS

The proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle feature segmentation with 3D Swin UNEt TRansfomer (UNETR); (2) generation of a 2D reformatted image containing the needle feature; (3) fine 2D needle feature segmentation with 2D Swin Transformer and calculation of 3D needle tip position and axis orientation. Pre-training and data augmentation were performed to improve network training. The pipeline was evaluated via cross-validation with 49 in vivo intra-procedural 3D MR images from preclinical pig experiments. The needle tip and axis localization errors were compared with human intra-reader variation using the Wilcoxon signed rank test, with p < 0.05 considered significant.

RESULTS

The average end-to-end computational time for the pipeline was 6 s per 3D volume. The median Dice scores of the 3D Swin UNETR and 2D Swin Transformer in the pipeline were 0.80 and 0.93, respectively. The median 3D needle tip and axis localization errors were 1.48 mm (1.09 pixels) and 0.98°, respectively. Needle tip localization errors were significantly smaller than human intra-reader variation (median 1.70 mm; p < 0.01).

CONCLUSION

The proposed automatic pipeline achieved rapid pixel-level 3D needle localization on intra-procedural 3D MRI without requiring a large 3D training dataset and has the potential to assist MRI-guided percutaneous interventions.

Advancements in Robotic-Assisted Spine Surgery

Applications and workflows around spinal robotics have evolved since these systems were first introduced in 2004. Initially approved for lumbar pedicle screw placement, the scope of robotics has expanded to instrumentation across different regions. Additionally, precise navigation can aid in tumor resection or spinal lesion ablation. Robot-assisted surgery can improve accuracy while decreasing radiation exposure, length of hospital stay, complication, and revision rates. Disadvantages include increased operative time, dependence on preoperative imaging among others. The future of robotic spine surgery includes automated surgery, telerobotic surgery, and the inclusion of machine learning or artificial intelligence in preoperative planning.

Developments and Challenges of Miniature Piezoelectric Robots: A Review

Miniature robots have been widely studied and applied in the fields of search and rescue, reconnaissance, micromanipulation, and even the interior of the human body benefiting from their highlight features of small size, light weight, and agile movement. With the development of new smart materials, many functional actuating elements have been proposed to construct miniature robots. Compared with other actuating elements, piezoelectric actuating elements have the advantages of compact structure, high power density, fast response, high resolution, and no electromagnetic interference, which make them greatly suitable for actuating miniature robots, and capture the attentions and favor of numerous scholars. In this paper, a comprehensive review of recent developments in miniature piezoelectric robots (MPRs) is provided. The MPRs are classified and summarized in detail from three aspects of operating environment, structure of piezoelectric actuating element, and working principle. In addition, new manufacturing methods and piezoelectric materials in MPRs, as well as the application situations, are sorted out and outlined. Finally, the challenges and future trends of MPRs are evaluated and discussed. It is hoped that this review will be of great assistance for determining appropriate designs and guiding future developments of MPRs, and provide a destination board to the researchers interested in MPRs.

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.

Stereo vision-based Kinematic calibration method for the Stewart platforms

Accuracy is the most important index for the industrial applications of the Stewart platform, which can be guaranteed by the kinematic calibration method to improve the motion orbit performance of this platform. In order to improve the effectiveness of the least squares algorithm and the identified accuracy of the platform's geometric parameter errors, an applicab-le dimensionless error model based on the structural characteristics of the Stewart platform is investigated. Moreover, a novel stereo vision-based measurement method is proposed, which can measure the 6-degree-of-freedom (DOF) pose of the moving platform. On this basis, an identification simulation is schemed to validate the efficiency of the dimensionless error model, and the kinematic calibration experiment is carried out on a prototype. The experimental results demonstrate that the position error is decreased to 0.261 mm with an improved accuracy of 89.720%, the orientation error is decreased to 0.051° with an improved accuracy of 90.351%.

State of the Art and Future Opportunities in MRI-Guided Robot-Assisted Surgery and Interventions

Magnetic resonance imaging (MRI) can provide high-quality 3-D visualization of target anatomy, surrounding tissue, and instrumentation, but there are significant challenges in harnessing it for effectively guiding interventional procedures. Challenges include the strong static magnetic field, rapidly switching magnetic field gradients, high-power radio frequency pulses, sensitivity to electrical noise, and constrained space to operate within the bore of the scanner. MRI has a number of advantages over other medical imaging modalities, including no ionizing radiation, excellent soft-tissue contrast that allows for visualization of tumors and other features that are not readily visible by other modalities, true 3-D imaging capabilities, including the ability to image arbitrary scan plane geometry or perform volumetric imaging, and capability for multimodality sensing, including diffusion, dynamic contrast, blood flow, blood oxygenation, temperature, and tracking of biomarkers. The use of robotic assistants within the MRI bore, alongside the patient during imaging, enables intraoperative MR imaging (iMRI) to guide a surgical intervention in a closed-loop fashion that can include tracking of tissue deformation and target motion, localization of instrumentation, and monitoring of therapy delivery. With the ever-expanding clinical use of MRI, MRI-compatible robotic systems have been heralded as a new approach to assist interventional procedures to allow physicians to treat patients more accurately and effectively. Deploying robotic systems inside the bore synergizes the visual capability of MRI and the manipulation capability of robotic assistance, resulting in a closed-loop surgery architecture. This article details the challenges and history of robotic systems intended to operate in an MRI environment and outlines promising clinical applications and associated state-of-the-art MRI-compatible robotic systems and technology for making this possible.

Design of a 6-DoF Parallel Robotic Platform for MRI Applications

In this work, the design, analysis, and characterization of a parallel robotic motion generation platform with 6-degrees of freedom (DoF) for magnetic resonance imaging (MRI) applications are presented. The motivation for the development of this robot is the need for a robotic platform able to produce accurate 6-DoF motion inside the MRI bore to serve as the ground truth for motion modeling; other applications include manipulation of interventional tools such as biopsy and ablation needles and ultrasound probes for therapy and neuromodulation under MRI guidance. The robot is comprised of six pneumatic cylinder actuators controlled via a robust sliding mode controller. Tracking experiments of the pneumatic actuator indicates that the system is able to achieve an average error of 0.69 ± 0.14 mm and 0.67 ± 0.40 mm for step signal tracking and sinusoidal signal tracking, respectively. To demonstrate the feasibility and potential of using the proposed robot for minimally invasive procedures, a phantom experiment was performed in the benchtop environment, which showed a mean positional error of 1.20 ± 0.43 mm and a mean orientational error of 1.09 ± 0.57°, respectively. Experiments conducted in a 3T whole body human MRI scanner indicate that the robot is MRI compatible and capable of achieving positional error of 1.68 ± 0.31 mm and orientational error of 1.51 ± 0.32° inside the scanner, respectively. This study demonstrates the potential of this device to enable accurate 6-DoF motions in the MRI environment.

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation

Piezoelectric actuators (PEA) are devices that are used for nano- microdisplacement due to their high precision, but one of the major issues is the non-linearity phenomena caused by the hysteresis effect, which diminishes the positioning performance. This study presents a novel control structure in order to reduce the hysteresis effect and increase the PEA performance by using a fuzzy logic control (FLC) combined with a Hammerstein–Wiener (HW) black-box mapping as a feedforward (FF) compensation. In this research, a proportional-integral-derivative (PID) was contrasted with an FLC. From this comparison, the most accurate was taken and tested with a complex structure with HW-FF to verify the accuracy with the increment of complexity. All of the structures were implemented in a dSpace platform to control a commercial Thorlabs PEA. The tests have shown that an FLC combined with HW was the most accurate, since the FF compensate the hysteresis and the FLC reduced the errors; the integral of the absolute error (IAE), the root-mean-square error (RMSE), and relative root-mean-square-error (RRMSE) for this case were reduced by several magnitude orders when compared to the feedback structures. As a conclusion, a complex structure with a novel combination of FLC and HW-FF provided an increment in the accuracy for a high-precision PEA.