Evaluation of robot-assisted MRI-guided prostate biopsy: needle path analysis during clinical trials

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.

Bevel-Tip Needle Deflection Modeling, Simulation, and Validation in Multi-Layer Tissues

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes as an effective alternative to more invasive surgical procedures. However, the outcome of needle-based approaches relies heavily on the accuracy of needle placement, which remains a challenge even with robot assistance and medical imaging guidance due to needle deflection caused by contact with soft tissues. In this paper, we present a novel mechanics-based 2D bevel-tip needle model that can account for the effect of nonlinear strain-dependent behavior of biological soft tissues under compression. Real-time finite element simulation allows multiple control inputs along the length of the needle with full three-degree-of-freedom (DOF) planar needle motions. Cross-validation studies using custom-designed multi-layer tissue phantoms as well as heterogeneous chicken breast tissues result in less than 1mm in-plane errors for insertions reaching depths of up to 61 mm, demonstrating the validity and generalizability of the proposed method.

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.

Motorized template for MRI-guided focal cryoablation of prostate cancer

MR-guided focal cryoablation of prostate cancer has often been selected as a minimally-invasive treatment option. Placing multiple cryo-needles accurately to form an ablation volume that adequately covers the target volume is crucial for better oncological/functional outcomes. This paper presents an MRI-compatible system combining a motorized tilting grid template with insertion depth sensing capabilities, enabling the physician to precisely place the cryo-needles into the desired location. In vivo animal study in a swine model (3 animals) was performed to test the device performance including targeting accuracy and the procedure workflow. The study showed that the insertion depth feedback improved the 3D targeting accuracy when compared to the conventional insertion technique (7.4 mm vs. 11.2 mm, p=0.04). All three cases achieved full iceball coverage without repositioning the cryo-needles. The results demonstrate the advantages of the motorized tilting mechanism and real-time insertion depth feedback, as well as the feasibility of the proposed workflow for MRI-guided focal cryoablation of prostate cancer.

A systematic review of image-guided, surgical robot-assisted percutaneous puncture: Challenges and benefits

Percutaneous puncture is a common medical procedure that involves accessing an internal organ or tissue through the skin. Image guidance and surgical robots have been increasingly used to assist with percutaneous procedures, but the challenges and benefits of these technologies have not been thoroughly explored. The aims of this systematic review are to furnish an overview of the challenges and benefits of image-guided, surgical robot-assisted percutaneous puncture and to provide evidence on this approach. We searched several electronic databases for studies on image-guided, surgical robot-assisted percutaneous punctures published between January 2018 and December 2022. The final analysis refers to 53 studies in total. The results of this review suggest that image guidance and surgical robots can improve the accuracy and precision of percutaneous procedures, decrease radiation exposure to patients and medical personnel and lower the risk of complications. However, there are many challenges related to the use of these technologies, such as the integration of the robot and operating room, immature robotic perception, and deviation of needle insertion. In conclusion, image-guided, surgical robot-assisted percutaneous puncture offers many potential benefits, but further research is needed to fully understand the challenges and optimize the utilization of these technologies in clinical practice.

Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention

PURPOSE

Understanding the three-dimensional anatomy of percutaneous intervention in prostate cancer is essential to avoid complications. Recently, attempts have been made to use machine learning to automate the segmentation of functional structures such as the prostate gland, rectum, and bladder. However, a paucity of material is available to segment extracapsular structures that are known to cause needle deflection during percutaneous interventions. This research aims to explore the feasibility of the automatic segmentation of prostate and extracapsular structures to predict needle deflection.

METHODS

Using pelvic magnetic resonance imagings (MRIs), 3D U-Net was trained and optimized for the prostate and extracapsular structures (bladder, rectum, pubic bone, pelvic diaphragm muscle, bulbospongiosus muscle, bull of the penis, ischiocavernosus muscle, crus of the penis, transverse perineal muscle, obturator internus muscle, and seminal vesicle). The segmentation accuracy was validated by putting intra-procedural MRIs into the 3D U-Net to segment the prostate and extracapsular structures in the image. Then, the segmented structures were used to predict deflected needle path in in-bore MRI-guided biopsy using a model-based approach.

RESULTS

The 3D U-Net yielded Dice scores to parenchymal organs (0.61-0.83), such as prostate, bladder, rectum, bulb of the penis, crus of the penis, but lower in muscle structures (0.03-0.31), except and obturator internus muscle (0.71). The 3D U-Net showed higher Dice scores for functional structures ([Formula: see text]0.001) and complication-related structures ([Formula: see text]0.001). The segmentation of extracapsular anatomies helped to predict the deflected needle path in MRI-guided prostate interventions of the prostate with the accuracy of 0.9 to 4.9 mm.

CONCLUSION

Our segmentation method using 3D U-Net provided an accurate anatomical understanding of the prostate and extracapsular structures. In addition, our method was suitable for segmenting functional and complication-related structures. Finally, 3D images of the prostate and extracapsular structures could simulate the needle pathway to predict needle deflections.

MRI-compatible electromagnetic servomotor for image-guided medical robotics

The soft-tissue imaging capabilities of magnetic resonance imaging (MRI) combined with high precision robotics has the potential to improve the precision and safety of a wide range of image-guided medical procedures. However, functional MRI-compatible robotics have not yet been realized in part because conventional electromagnetic servomotors can become dangerous projectiles near the strong magnetic field of an MRI scanner. Here we report an electromagnetic servomotor constructed from non-magnetic components, where high-torque and controlled rotary actuation is produced via interaction between electrical current in the servomotor armature and the magnetic field generated by the superconducting magnet of the MRI scanner itself. Using this servomotor design, we then build and test an MRI-compatible robot which can achieve the linear forces required to insert a large-diameter biopsy instrument in tissue during simultaneous MRI. Our electromagnetic servomotor can be safely operated (while imaging) in the patient area of a 3 Tesla clinical MRI scanner.

The Impact of Placement Errors on the Tumor Coverage in MRI-Guided Focal Cryoablation of Prostate Cancer

RATIONALE AND OBJECTIVES

There have been multiple investigations defining and reporting the effectiveness of focal cryoablation as a treatment option for organ-confined prostate cancer. However, the impact of cryo-needle/probe placement accuracy within the tumor and gland has not been extensively studied. We analyzed how variations in the placement of the cryo-needles, specifically errors leading to incomplete ablation, may affect prostate cancer's resulting cryoablation.

MATERIALS AND METHODS

We performed a study based on isothermal models using Monte Carlo simulations to analyze the impact of needle placement errors on tumor coverage and the probability of positive ablation margin. We modeled the placement error as a Gaussian noise on the cryo-needle position. The analysis used retrospective MRI data of 15 patients with biopsy-proven, unifocal, and MRI visible prostate cancer to calculate the impact of placement error on the volume of the tumor encompassed by the -40°C and -20°C isotherms using one to four cryo-needles.

RESULTS

When the standard deviation of the placement error reached 3 mm, the tumor coverage was still above 97% with the -20°C isotherm, and above 81% with the -40°C isotherm using two cryo-needles or more. The probability of positive margin was significantly lower considering the -20°C isotherm (0.04 for three needles) than using the -40°C isotherm (0.66 for three needles).

CONCLUSION

The results indicated that accurate cryo-needle placement is essential for the success of focal cryoablation of prostate cancer. The analysis shows that an admissible targeting error depends on the lethal temperature considered and the number of cryo-needles used.

In vivo evaluation of angulated needle-guide template for MRI-guided transperineal prostate biopsy

PURPOSE

Magnetic resonance imaging (MRI)-guided transperineal prostate biopsy has been practiced since the early 2000s. The technique often suffers from targeting error due to deviation of the needle as a result of physical interaction between the needle and inhomogeneous tissues. Existing needle guide devices, such as a grid template, do not allow choosing an alternative insertion path to mitigate the deviation because of their limited degree-of-freedom (DoF). This study evaluates how an angulated needle insertion path can reduce needle deviation and improve needle placement accuracy.

METHODS

We extended a robotic needle-guidance device (Smart Template) for in-bore MRI-guided transperineal prostate biopsy. The new Smart Template has a 4-DoF needle-guiding mechanism allowing a translational range of motion of 65 and 58 mm along the vertical and horizontal axis, and a needle rotational motion around the vertical and horizontal axis ± 30 ∘ and a vertical rotational range of - 30 ∘ , + 10 ∘ , respectively. We defined a path planning strategy, which chooses between straight and angulated insertion paths depending on the anatomical structures on the potential insertion path. We performed (a) a set of experiments to evaluate the device positioning accuracy outside the MR-bore, and (b) an in vivo experiment to evaluate the improvement of targeting accuracy combining straight and angulated insertions in animal models (swine, n = 3 ).

RESULTS

We analyzed 46 in vivo insertions using either straight or angulated insertions paths. The experiment showed that the proposed strategy of selecting straight or angulated insertions based on the subject's anatomy outperformed the conventional approach of just straight insertions in terms of targeting accuracy (2.4 mm [1.3-3.7] vs 3.9 mm [2.4-5.0] {Median IQR } ); p = 0.041 after the bias correction).

CONCLUSION

The in vivo experiment successfully demonstrated that an angulated needle insertion path could improve needle placement accuracy with a path planning strategy that takes account of the subject-specific anatomical structures.

Automatic needle tracking using Mask R-CNN for MRI-guided percutaneous interventions

PURPOSE

Accurate needle tracking provides essential information for MRI-guided percutaneous interventions. Passive needle tracking using MR images is challenged by variations of the needle-induced signal void feature in different situations. This work aimed to develop an automatic needle tracking algorithm for MRI-guided interventions based on the Mask Region Proposal-Based Convolutional Neural Network (R-CNN).

METHODS

Mask R-CNN was adapted and trained to segment the needle feature using 250 intra-procedural images from 85 MRI-guided prostate biopsy cases and 180 real-time images from MRI-guided needle insertion in ex vivo tissue. The segmentation masks were passed into the needle feature localization algorithm to extract the needle feature tip location and axis orientation. The proposed algorithm was tested using 208 intra-procedural images from 40 MRI-guided prostate biopsy cases, and 3 real-time MRI datasets in ex vivo tissue. The algorithm results were compared with human-annotated references.

RESULTS

In prostate datasets, the proposed algorithm achieved needle feature tip localization error with median Euclidean distance (dxy) of 0.71 mm and median difference in axis orientation angle (dθ) of 1.28°, respectively. In 3 real-time MRI datasets, the proposed algorithm achieved consistent dynamic needle feature tracking performance with processing time of 75 ms/image: (a) median dxy = 0.90 mm, median dθ = 1.53°; (b) median dxy = 1.31 mm, median dθ = 1.9°; (c) median dxy = 1.09 mm, median dθ = 0.91°.

CONCLUSIONS

The proposed algorithm using Mask R-CNN can accurately track the needle feature tip and axis on MR images from in vivo intra-procedural prostate biopsy cases and ex vivo real-time MRI experiments with a range of different conditions. The algorithm achieved pixel-level tracking accuracy in real time and has potential to assist MRI-guided percutaneous interventions.

Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy

OBJECTIVE

Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach.

METHODS

We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE).

RESULTS

Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm.

CONCLUSION

SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.