In Vivo Feasibility Study: Evaluating Autonomous Data-Driven Robotic Needle Trajectory Correction in MRI-Guided Transperineal Procedures

This study addresses the targeting challenges in MRI-guided transperineal needle placement for prostate cancer (PCa) diagnosis and treatment, a procedure where accuracy is crucial for effective outcomes. We introduce a parameter-agnostic trajectory correction approach incorporating a data-driven closed-loop strategy by radial displacement and an FBG-based shape sensing to enable autonomous needle steering. In an animal study designed to emulate clinical complexity and assess MRI compatibility through a PCa mock biopsy procedure, our approach demonstrated a significant improvement in targeting accuracy (p<0.05), with mean target error of only 2.2 ± 1.9 mm on first insertion attempts, without needle reinsertions. To the best of our knowledge, this work represents the first in vivo evaluation of robotic needle steering with FBG-sensor feedback, marking a significant step towards its clinical translation.

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.

A review on tissue-needle interaction and path planning models for bevel tip type flexible needle minimal intervention

A flexible needle has emerged as a crucial clinical technique in contemporary medical practices, particularly for minimally invasive interventions. Its applicability spans diverse surgical domains such as brachytherapy, cardiovascular surgery, neurosurgery and others. Notably, flexible needles find utility in biopsies requiring deep skin penetration to access infected areas. Despite its minimally invasive advantages, the precise guidance of the needle to its intended target, while avoiding damage to bones, blood vessels, organs and tissues, remains a significant challenge for researchers. Consequently, extensive research has been dedicated to enhancing the steering and accuracy of flexible needles. Here, we aim to elucidate the recent advancements, trends and perspectives in flexible needle steering models and path planning over the last 15 years. The discussed models encompass various types, including symmetric-tip needles, curved-tip needles, tendon-actuated needles, programmable needles and the innovative fracture-directed waterjet needles. Moreover, the paper offers a comprehensive analysis, comparing the trajectories followed by these needle models to attain the desired target with minimal tissue damage. By delving into these aspects, the paper contributes to a deeper understanding of the current landscape of flexible needle technology and guides future research directions in this dynamic field.

Data-driven adaptive needle insertion assist for transperineal prostate interventions

Objective.Clinical outcomes of transperineal prostate interventions, such as biopsy, thermal ablations, and brachytherapy, depend on accurate needle placement for effectiveness. However, the accurate placement of a long needle, typically 150-200 mm in length, is challenging due to needle deviation induced by needle-tissue interaction. While several approaches for needle trajectory correction have been studied, many of them do not translate well to practical applications due to the use of specialized needles not yet approved for clinical use, or to relying on needle-tissue models that need to be tailored to individual patients.Approach.In this paper, we present a robot-assisted collaborative needle insertion method that only requires an actuated passive needle guide and a conventional needle. The method is designed to assist a physician inserting a needle manually through a needle guide. If the needle is deviated from the intended path, actuators shifts the needle radially in order to steer the needle trajectory and compensate for needle deviation adaptively. The needle guide is controlled by a new data-driven algorithm which does not requirea prioriinformation about needle or tissue properties. The method was evaluated in experiments with bothin vitroandex vivophantoms.Main results.The experiments inex vivotissue reported a mean final placement error of 0.36 mm with a reduction of 96.25% of placement error when compared to insertions without the use of assistive correction.Significance.Presented results show that the proposed closed-loop formulation can be successfully used to correct needle deflection during collaborative manual insertion with potential to be easily translated into clinical application.

Impedance Variation and Learning Strategies in Human-Robot Interaction

In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.

A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy

Purpose

Low-dose-rate brachytherapy is a key treatment for low-risk or favorable intermediate-risk prostate cancer. The number of radioactive seeds inserted during the procedure depends on prostate volume, and is not easy to predict without pre-planning. Consequently, a large number of unused seeds may be left after treatment. The objective of the present study was to predict the exact number of seeds for future patients using machine learning and a database of 409 treatments.

Material and methods

Database consisted of 18 dosimetric and efficiency parameters for each of 409 cases. Nine predictive algorithms based on machine-learning were compared in this database, which was divided into training group (80%) and test group (20%). Ten-fold cross-validation was applied to obtain robust statistics. The best algorithm was then used to build an abacus able to predict number of implanted seeds from expected prostate volume only. As an evaluation, the abacus was also applied on an independent series of 38 consecutive patients.

Results

The best coefficients of determination R² were given by support vector regression, with values attaining 0.928, 0.948, and 0.968 for training set, test set, and whole set, respectively. In terms of predicted seeds in test group, mean square error, median absolute error, mean absolute error, and maximum error were 2.55, 0.92, 1.21, and 7.29, respectively. The use of obtained abacus in 38 additional patients resulted in saving of 493 seeds (393 vs. 886 remaining seeds).

Conclusions

Machine-learning-based abacus proposed in this study aims at estimating the necessary number of seeds for future patients according to past experience. This new abacus, based on 409 treatments and successfully tested in 38 new patients, is a good alternative to non-specific recommendations.

A review of the research progress of interventional medical equipment and methods for prostate cancer

BACKGROUND

Prostate cancer is a common disease in men and has a relatively high mortality rate. However, the interventional medical equipment used for prostate biopsy and brachytherapy has always been a social concern.

METHODS

To understand interventional medical equipment for prostate cancer, the structure of manual, semi-automatic and automatic medical equipment were considered as the mainline, while the corresponding research on these structures were the auxiliary lines. The characteristics and corresponding research status have been discussed.

RESULTS

Interventional medical equipment for prostate cancer with different degrees of automation and its characteristics were determined, and the imaging principles and characteristics of computed tomography, transrectal ultrasound and magnetic resonance imaging have been briefly described.

CONCLUSION

Certain feasible research suggestions have been proposed for future development from the perspective of structure, accuracy and safety. These include flexible and compact robot structures, high-precision image recognition and guidance, accurate dose planning and monitoring, real-time imaging monitoring without delay, high-precision needle insertion strategy, master-slave control, virtual reality and remote control.

Design, Fabrication, and Testing of a Flexible Three-Dimensional Printed Percutaneous Needle With Embedded Actuators

Percutaneous needle-based procedures have replaced open surgeries in cancer treatments to perform the tasks with minimal invasiveness to the tissue. Precise placement of the needle at target positions in cancer diagnostic (e.g., breast biopsy) or therapeutic (e.g., prostate brachytherapy) procedures governs the success of such procedures. Also, in many needle insertion applications, it is desired to steer away from critical organs or to maneuver around anatomical obstacles in tissue. This work introduces a flexible three-dimensional (3D) printed percutaneous needle with embedded actuators for improved navigation inside the tissue toward the target. The needle is manipulated via a programmed portable motorized control unit to realize an average angular deflection of about 15 and 14 deg in air and a tissue-mimicking phantom, respectively. We demonstrated the needle's capability to reach the target, while avoiding obstacles. We also demonstrated that the flexible needle can be guided through a desired trajectory by controlling its angular deflection and axial movement. The 3D deflection of the needle is expected to assist in breast cancer lumpectomy for multiple extractions of tissue samples or in prostate brachytherapy via a curvilinear approach. The flexible needle may help reducing the complexity of current path planning algorithms, and thereby improve efficiency of closed-loop control systems in needle steering.

Optimal Localization of a Novel Shifted 1T-Ring Based Microwave Ablation Probe in Hepatocellular Carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is one of the most dangerous, and fatal cancers. Thermal ablation proved its power as the best treatment method for HCC. In microwave thermal ablation, microwave probes are used to generate electromagnetic waves (EMW) at microwave (MW) frequency 2.45 GHz. In this paper, the design/model of a novel microwave ablation probe, namely a single slot with a shifted 1T-ring probe is presented for HCC therapy.

METHODS

A Finite Element Method (FEM) is employed to model the probe and the hepatic tumor liver tissues. The relation between the tip of probe position and the center of the hepatic tumor was studied to determine the best probe location at which a minimum MW power is required to ablate the entire tumor tissues with the smallest damage in the nearby healthy tissues to the tumor.

RESULTS

The results indicated that the ablated part of the tissues varies depending on the MW probe type, the amount of used power, the location of the probe, and the exposure time. Hepatic tumors' diameters from 2-5cm were studied.

CONCLUSION

It was shown that the proposed SSS 1T-ring (single slot with shifted 1T-ring) probe provided the best ablation performance when the probe's tip placed below the tumor's center by 11 mm, which achieved 100% damage in the tumor tissues using 6 W power for 10 minutes.

SIGNIFICANCE

When the probe's tip is located at the center of the tumor, the ablation rate was 73.45% in the tumor tissues under the same conditions.

Event-Triggered 3D Needle Control Using a Reduced-Order Computationally Efficient Bicycle Model in a Constrained Optimization Framework

Long flexible needles used in percutaneous procedures such as biopsy and brachytherapy deflect during insertion, thus reducing needle tip placement accuracy. This paper presents a surgeon-in-the-loop system to automatically steer the needle during manual insertion and compensate for needle deflection using an event-triggered controller. A reduced-order kinematic bicycle model incorporating needle tip measurement data from ultrasound images is used to determine steering actions required to minimize needle deflection. To this end, an analytic solution to the reduced-order bicycle model, which is shown to be more computationally efficient than a discrete-step implementation of the same model, is derived and utilized for needle tip trajectory prediction. These needle tip trajectory predictions are used online to optimize the insertion depths (event-trigger points) for steering actions such that needle deflection is minimized. The use of the analytic model and the event-triggered controller also allows for limiting the number and extent of needle rotations (to reduce tissue trauma) in a constrained optimization framework. The system was tested experimentally in three different ex-vivo tissue phantoms with a surgeon-in-the-loop needle insertion device. The proposed needle steering controller was shown to keep the average needle deflection within 0.47 [Formula: see text] 0.21[Formula: see text]mm at the final insertion depth of 120[Formula: see text]mm.

Needle placement errors: do we need steerable needles in interventional radiology?

Purpose

Accurate and precise needle placement is of utmost importance in interventional radiology. However, targeting can be challenging due to, eg, tissue motion and deformation. Steerable needles are a possible solution to overcome these challenges. The present work studied the clinical need for steerable needles. We aimed to answer three subquestions: 1) What are the current challenges in needle placement? 2) What are allowable needle placement errors? and 3) Do current needles need improvement and would steerable needles add clinical value?

Methods

A questionnaire was administered at the Annual Meeting of Cardiovascular and Interventional Radiology Society of Europe in 2016. In total, 153 respondents volunteered to fill out the survey, among them 125 (interventional) radiologists with experience in needle placement.

Results

1) Current challenges in needle placement include patient-specific and technical factors. Movement of the target due to breathing makes it most difficult to place a needle (90%). 2) The mean maximal allowable needle placement error in targeted lesions is 2.7 mm. A majority of the respondents (85%) encounter unwanted needle bending upon insertion. The mean maximal encountered unwanted needle bending is 5.3 mm. 3) Needles in interventional radiology need improvement, eg, improved needle visibility and manipulability, according to 95% of the respondents. Added value for steerable needles in current interventions is seen by 93% of the respondents.

Conclusion

Steerable needles have the potential to add clinical value to radiologic interventions. The current data can be used as input for defining clinical design requirements for technical tools, such as steerable needles and navigation models, with the aim to improve needle placement in interventional radiology.

Closed-Loop Active Compensation for Needle Deflection and Target Shift During Cooperatively Controlled Robotic Needle Insertion

Intra-operative imaging is sometimes available to assist needle biopsy, but typical open-loop insertion does not account for unmodeled needle deflection or target shift. Closed-loop image-guided compensation for deviation from an initial straight-line trajectory through rotational control of an asymmetric tip can reduce targeting error. Incorporating robotic closed-loop control often reduces physician interaction with the patient, but by pairing closed-loop trajectory compensation with hands-on cooperatively controlled insertion, a physician's control of the procedure can be maintained while incorporating benefits of robotic accuracy. A series of needle insertions were performed with a typical 18G needle using closed-loop active compensation under both fully autonomous and user-directed cooperative control. We demonstrated equivalent improvement in accuracy while maintaining physician-in-the-loop control with no statistically significant difference (p > 0.05) in the targeting accuracy between any pair of autonomous or individual cooperative sets, with average targeting accuracy of 3.56 mm_rms. With cooperatively controlled insertions and target shift between 1 and 10 mm introduced upon needle contact, the system was able to effectively compensate up to the point where error approached a maximum curvature governed by bending mechanics. These results show closed-loop active compensation can enhance targeting accuracy, and that the improvement can be maintained under user directed cooperative insertion.

Automatic Robotic Steering of Flexible Needles from 3D Ultrasound Images in Phantoms and Ex Vivo Biological Tissue

Robotic control of needle bending aims at increasing the precision of percutaneous procedures. Ultrasound feedback is preferable for its clinical ease of use, cost and compactness but raises needle detection issues. In this paper, we propose a complete system dedicated to robotized guidance of a flexible needle under 3D ultrasound imaging. This system includes a medical robot dedicated to transperineal needle positioning and insertion, a rapid path planning for needle steering using bevel-tip needle natural curvature in tissue, and an ultrasound-based automatic needle detection algorithm. Since ultrasound-based automatic needle steering is often made difficult by the needle localization in biological tissue, we quantify the benefit of using flexible echogenic needles for robotized guidance under 3D ultrasound. The "echogenic" term refers to the etching of microstructures on the needle shaft. We prove that these structures improve needle visibility and detection robustness in ultrasound images. We finally present promising results when reaching targets using needle steering. The experiments were conducted with various needles in different media (synthetic phantoms and ex vivo biological tissue). For instance, with nitinol needles the mean accuracy is 1.2 mm (respectively 3.8 mm) in phantoms (resp. biological tissue).

A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modelling

Global modelling has traditionally been the approach taken to estimate needle deflection in soft tissue. In this paper, we propose a new method based on local data-driven modelling of needle deflection. External measurement of needle-tissue interactions is collected from several insertions in ex vivo tissue to form a cloud of data. Inputs to the system are the needle insertion depth, axial rotations, and the forces and torques measured at the needle base by a force sensor. When a new insertion is performed, the just-in-time learning method estimates the model outputs given the current inputs to the needle-tissue system and the historical database. The query is compared to every observation in the database and is given weights according to some similarity criteria. Only a subset of historical data that is most relevant to the query is selected and a local linear model is fit to the selected points to estimate the query output. The model outputs the 3D deflection of the needle tip and the needle insertion force. The proposed approach is validated in ex vivo multilayered biological tissue in different needle insertion scenarios. Experimental results in five different case studies indicate an accuracy in predicting needle deflection of 0.81 and 1.24 mm in the horizontal and vertical lanes, respectively, and an accuracy of 0.5 N in predicting the needle insertion force over 216 needle insertions.