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

Evolution of Neurosurgical Robots: Historical Progress and Future Direction

In 1985, Professor KWOH first introduced robots into neurosurgery. Since then, advancements of stereotactic frames, radiographic imaging, and neuronavigation have led to the dominance of classic stereotactic robots. A comprehensive retrieval was performed using academic databases and search agents to acquire professional information, with a cutoff date of June, 2024. This reveals a multitude of emerging technologies are coming to the forefront, including tremor filtering, motion scaling, obstacle avoidance, force sensing, which have made significant contributions to the high efficiency, high precision, minimally invasive, and exact efficacy of robot-assisted neurosurgery. Those technologies have been applied in innovative magnetic resonance-compatible neurosurgical robots, such as Neuroarm and Neurobot, with real-time image-guided surgery. Despite these advancements, the major challenge is considered as magnetic resonance compatibility in terms of space, materials, driving, and imaging. Future research directions are anticipated to focus on 1) robotic precise perception; 2) artificial intelligence; and 3) the advancement of telesurgery.

7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery

Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.

Modeling and Control of an MR-Safe Pneumatic Radial Inflow Motor and Encoder (PRIME)

Magnetic resonance (MR) conditional actuators and encoders are the key components for MR-guided robotic systems. In this article, we present the modeling and control of our MR-safe pneumatic radial inflow motor and encoder. A comprehensive model is developed that considers the primary dynamic elements of the system, including: 1) motor dynamics, 2) pneumatic transmission line dynamics, and 3) valve dynamics. After model validation, we present a simplified third order model that facilitates design of a first order sliding mode controller (TO-SMC). Finally, the motor hardware is tested in a 7T MRI. No image distortion or artifacts were observed. We posit the MR-safe motor and dynamic model will lower the entry barriers for researchers interested in MR-guided robots and promote wider adoption of MR-guided robotic systems.

A Survey of Needle Steering Approaches in Minimally Invasive Surgery

In virtue of a curved insertion path inside tissues, needle steering techniques have revealed the potential with the assistance of medical robots and images. The superiority of this technique has been preliminarily verified with several maneuvers: target realignment, obstacle circumvention, and multi-target access. However, the momentum of needle steering approaches in the past decade leads to an open question-"How to choose an applicable needle steering approach for a specific clinical application?" This survey discusses this question in terms of design choices and clinical considerations, respectively. In view of design choices, this survey proposes a hierarchical taxonomy of current needle steering approaches. Needle steering approaches of different manipulations and designs are classified to systematically review the design choices and their influences on clinical treatments. In view of clinical consideration, this survey discusses the steerability and acceptability of the current needle steering approaches. On this basis, the pros and cons of the current needle steering approaches are weighed and their suitable applications are summarized. At last, this survey concluded with an outlook of the needle steering techniques, including the potential clinical applications and future developments in mechanical design.

Treatment Planning Strategies for Interstitial Ultrasound Ablation of Prostate Cancer

PURPOSE

To develop patient-specific 3D models using Finite-Difference Time-Domain (FDTD) simulations and pre-treatment planning tools for the selective thermal ablation of prostate cancer with interstitial ultrasound. This involves the integration with a FDA 510(k) cleared catheter-based ultrasound interstitial applicators and delivery system.

METHODS

A 3D generalized "prostate" model was developed to generate temperature and thermal dose profiles for different applicator operating parameters and anticipated perfusion ranges. A priori planning, based upon these pre-calculated lethal thermal dose and iso-temperature clouds, was devised for iterative device selection and positioning. Full 3D patient-specific anatomic modeling of actual placement of single or multiple applicators to conformally ablate target regions can be applied, with optional integrated pilot-point temperature-based feedback control and urethral/rectum cooling. These numerical models were verified against previously reported ex-vivo experimental results obtained in soft tissues.

RESULTS

For generic prostate tissue, 360 treatment schemes were simulated based on the number of transducers (1-4), applied power (8-20 W/cm2), heating time (5, 7.5, 10 min), and blood perfusion (0, 2.5, 5 kg/m3/s) using forward treatment modelling. Selectable ablation zones ranged from 0.8-3.0 cm and 0.8-5.3 cm in radial and axial directions, respectively. 3D patient-specific thermal treatment modeling for 12 Cases of T2/T3 prostate disease demonstrate applicability of workflow and technique for focal, quadrant and hemi-gland ablation. A temperature threshold (e.g., Tthres = 52 °C) at the treatment margin, emulating placement of invasive temperature sensing, can be applied for pilot-point feedback control to improve conformality of thermal ablation. Also, binary power control (e.g., Treg = 45 °C) can be applied which will regulate the applied power level to maintain the surrounding temperature to a safe limit or maximum threshold until the set heating time.

CONCLUSIONS

Prostate-specific simulations of interstitial ultrasound applicators were used to generate a library of thermal-dose distributions to visually optimize and set applicator positioning and directivity during a priori treatment planning pre-procedure. Anatomic 3D forward treatment planning in patient-specific models, along with optional temperature-based feedback control, demonstrated single and multi-applicator implant strategies to effectively ablate focal disease while affording protection of normal tissues.

Effect of Magnetic Resonance Imaging on the Motion Accuracy of Magnetic Resonance Imaging-compatible Focused Ultrasound Robotic System

Purpose

The current study provides insights into the challenges of safely operating a magnetic resonance imaging (MRI)-guided focused ultrasound (MRgFUS) robotic system in a high-field MRI scanner in terms of robotic motion accuracy.

Materials and Methods

Grid sonications were carried out in phantoms and excised porcine tissue in a 3T MRI scanner using an existing MRgFUS robotic system. Fast low-angle shot-based magnetic resonance thermometry was employed for the intraprocedural monitoring of thermal distribution.

Results

Strong shifting of the heated spots from the intended points was observed owing to electromagnetic interference (EMI)-induced malfunctions in system's operation. Increasing the slice thickness of the thermometry sequence to at least 8 mm was proven an efficient method for preserving the robotic motion accuracy.

Conclusions

These findings raise awareness about EMI effects on the motion accuracy of MRgFUS robotic devices and how they can be mitigated by employing suitable thermometry parameters.

Biochemical implications of robotic surgery: a new frontier in the operating room

Robotic surgery represents a milestone in surgical procedures, offering advantages such as less invasive methods, elimination of tremors, scaled motion, and 3D visualization. This in-depth analysis explores the complex biochemical effects of robotic methods. The use of pneumoperitoneum and steep Trendelenburg positioning can decrease pulmonary compliance and splanchnic perfusion while increasing hypercarbia. However, robotic surgery reduces surgical stress and inflammation by minimizing tissue trauma. This contributes to faster recovery but may limit immune function. Robotic procedures also limit ischemia-reperfusion injury and oxidative damage compared to open surgery. They also help preserve native antioxidant defenses and coagulation. In a clinical setting, robotic procedures reduce blood loss, pain, complications, and length of stay compared to traditional procedures. However, risks remain, including device failure, the need for conversion to open surgery and increased costs. On the oncology side, there is still debate about margins, recurrence, and long-term survival. The advent of advanced technologies, such as intraoperative biosensors, localized drug delivery systems, and the incorporation of artificial intelligence, may further improve the efficiency of robotic surgery. However, ethical dilemmas regarding patient consent, privacy, access, and regulation of this disruptive innovation need to be addressed. Overall, this review sheds light on the complex biochemical implications of robotic surgery and highlights areas that require additional mechanistic investigation. It presents a comprehensive approach to responsibly maximize the potential of robotic surgery to improve patient outcomes, integrating technical skill with careful consideration of physiological and ethical issues.

A magnetic resonance conditional robot for lumbar spinal injection: Development and preliminary validation

PURPOSE

This work presents the design and preliminary validation of a Magnetic Resonance (MR) conditional robot for lumbar injection for the treatment of lower back pain.

METHODS

This is a 4-degree-of-freedom (DOF) robot that is 200 × 230 × 130 mm3 in volume and has a mass of 0.8 kg. Its lightweight and compact features allow it to be directly affixed to patient's back, establishing a rigid connection, thus reducing positional errors caused by patient movements during treatment.

RESULTS

To validate the positioning accuracy of the needle by the robot, an electromagnetic (EM) tracking system and a needle with an EM sensor embedded in the tip were used for the free space evaluation with position accuracy of 0.88 ± 0.46 mm and phantom mock insertions using the Loop-X CBCT scanner with target position accuracy of 3.62 ± 0.92 mm.

CONCLUSION

Preliminary experiments demonstrated that the proposed robot showed improvements and benefits in its rotation range, flexible needle adjustment, and sensor protection compared with previous and existing systems, offering broader clinical applications.

A deep unrolled neural network for real-time MRI-guided brain intervention

Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI). Here, we proposed a deep unrolled neural network, dubbed as LSFP-Net, for real-time i-MRI reconstruction. By integrating LSFP-Net and a custom-designed, MR-compatible interventional device into a 3 T MRI scanner, a real-time MRI-guided brain intervention system is proposed. The performance of the system was evaluated using phantom and cadaver studies. 2D/3D real-time i-MRI was achieved with temporal resolutions of 80/732.8 ms, latencies of 0.4/3.66 s including data communication, processing and reconstruction time, and in-plane spatial resolution of 1 × 1 mm2. The results demonstrated that the proposed method enables real-time monitoring of the remote-controlled brain intervention, and showed the potential to be readily integrated into diagnostic scanners for image-guided neurosurgery.

Safe physical interaction with cobots: a multi-modal fusion approach for health monitoring

Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services.

Non-Metallic MR-Guided Concentric Tube Robot for Intracerebral Hemorrhage Evacuation

OBJECTIVE

We aim to develop and evaluate an MR-conditional concentric tube robot for intracerebral hemorrhage (ICH) evacuation.

METHODS

We fabricated the concentric tube robot hardware with plastic tubes and customized pneumatic motors. The robot kinematic model was developed using a discretized piece-wise constant curvature (D-PCC) approach to account for variable curvature along the tube shape, and tube mechanics model was used to compensate torsional deflection of the inner tube. The MR-safe pneumatic motors were controlled using a variable gain PID algorithm. The robot hardware was validated in a series of bench-top and MRI experiments, and the robot's evacuation efficacy was tested in MR-guided phantom trials.

RESULTS

The pneumatic motor was able to achieve a rotational accuracy of 0.32°±0.30° with the proposed variable gain PID control algorithm. The kinematic model provided a positional accuracy of the tube tip of 1.39 ± 0.54 mm. The robot was able to evacuate an initial 38.36 mL clot, leaving a residual hematoma of 8.14 mL after 5 minutes, well below the 15 mL guideline suggesting good post-ICH evacuation clinical outcomes.

CONCLUSION

This robotic platform provides an effective method for MR-guided ICH evacuation.

SIGNIFICANCE

ICH evacuation is feasible under MRI guidance using a plastic concentric tube, indicating potential feasibility in future live animal studies.

Image-guided cancer surgery: a narrative review on imaging modalities and emerging nanotechnology strategies

Surgical resection is the cornerstone of solid tumour treatment. Current techniques for evaluating margin statuses, such as frozen section, imprint cytology, and intraoperative ultrasound, are helpful. However, an intraoperative assessment of tumour margins that is accurate and safe is clinically necessary. Positive surgical margins (PSM) have a well-documented negative effect on treatment outcomes and survival. As a result, surgical tumour imaging methods are now a practical method for reducing PSM rates and improving the efficiency of debulking surgery. Because of their unique characteristics, nanoparticles can function as contrast agents in image-guided surgery. While most image-guided surgical applications utilizing nanotechnology are now in the preclinical stage, some are beginning to reach the clinical phase. Here, we list the various imaging techniques used in image-guided surgery, such as optical imaging, ultrasound, computed tomography, magnetic resonance imaging, nuclear medicine imaging, and the most current developments in the potential of nanotechnology to detect surgical malignancies. In the coming years, we will see the evolution of nanoparticles tailored to specific tumour types and the introduction of surgical equipment to improve resection accuracy. Although the promise of nanotechnology for producing exogenous molecular contrast agents has been clearly demonstrated, much work remains to be done to put it into practice.

Current State of Robotics in Interventional Radiology

As a relatively new specialty with a minimally invasive nature, the field of interventional radiology is rapidly growing. Although the application of robotic systems in this field shows great promise, such as with increased precision, accuracy, and safety, as well as reduced radiation dose and potential for teleoperated procedures, the progression of these technologies has been slow. This is partly due to the complex equipment with complicated setup procedures, the disruption to theatre flow, the high costs, as well as some device limitations, such as lack of haptic feedback. To further assess these robotic technologies, more evidence of their performance and cost-effectiveness is needed before their widespread adoption within the field. In this review, we summarise the current progress of robotic systems that have been investigated for use in vascular and non-vascular interventions.

Review of Robot-Assisted HIFU Therapy

This paper provides an overview of current robot-assisted high-intensity focused ultrasound (HIFU) systems for image-guided therapies. HIFU is a minimally invasive technique that relies on the thermo-mechanical effects of focused ultrasound waves to perform clinical treatments, such as tumor ablation, mild hyperthermia adjuvant to radiation or chemotherapy, vein occlusion, and many others. HIFU is typically performed under ultrasound (USgHIFU) or magnetic resonance imaging guidance (MRgHIFU), which provide intra-operative monitoring of treatment outcomes. Robot-assisted HIFU probe manipulation provides precise HIFU focal control to avoid damage to surrounding sensitive anatomy, such as blood vessels, nerve bundles, or adjacent organs. These clinical and technical benefits have promoted the rapid adoption of robot-assisted HIFU in the past several decades. This paper aims to present the recent developments of robot-assisted HIFU by summarizing the key features and clinical applications of each system. The paper concludes with a comparison and discussion of future perspectives on robot-assisted HIFU.

Flexible Needle Steering with Tethered and Untethered Actuation: Current States, Targeting Errors, Challenges and Opportunities

Accurate needle targeting is critical for many clinical procedures, such as transcutaneous biopsy or radiofrequency ablation of tumors. However, targeting errors may arise, limiting the widespread adoption of these procedures. Advances in flexible needle (FN) steering are emerging to mitigate these errors. This review summarizes the state-of-the-art developments of FNs and addresses possible targeting errors that can be overcome with steering actuation techniques. FN steering techniques can be classified as passive and active. Passive steering directly results from the needle-tissue interaction forces, whereas active steering requires additional forces to be applied at the needle tip, which enhances needle steerability. Therefore, the corresponding targeting errors of most passive FNs and active FNs are between 1 and 2 mm, and less than 1 mm, respectively. However, the diameters of active FNs range from 1.42 to 12 mm, which is larger than the passive steering needle varying from 0.5 to 1.4 mm. Therefore, the development of active FNs is an area of active research. These active FNs can be steered using tethered internal direct actuation or untethered external actuation. Examples of tethered internal direct actuation include tendon-driven, longitudinal segment transmission and concentric tube transmission. Tendon-driven FNs have various structures, and longitudinal segment transmission needles could be adapted to reduce tissue damage. Additionally, concentric tube needles have immediate advantages and clinical applications in natural orifice surgery. Magnetic actuation enables active FN steering with untethered external actuation and facilitates miniaturization. The challenges faced in the fabrication, sensing, and actuation methods of FN are analyzed. Finally, bio-inspired FNs may offer solutions to address the challenges faced in FN active steering mechanisms.

Flexible Needle Bending Model for Spinal Injection Procedures

An in situ needle manipulation technique used by physicians when performing spinal injections is modeled to study its effect on needle shape and needle tip position. A mechanics-based model is proposed and solved using finite element method. A test setup is presented to mimic the needle manipulation motion. Tissue phantoms made from plastisol as well as porcine skeletal muscle samples are used to evaluate the model accuracy against medical images. The effect of different compression models as well as model parameters on model accuracy is studied, and the effect of needle-tissue interaction on the needle remote center of motion is examined. With the correct combination of compression model and model parameters, the model simulation is able to predict needle tip position within submillimeter accuracy.

Sensor-Based Automated Detection of Electrosurgical Cautery States

In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools' location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery-robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.