Tactics: an open-source platform for planning, simulating and validating stereotactic surgery

Automatic Path-Planning Techniques for Minimally Invasive Stereotactic Neurosurgical Procedures-A Systematic Review

This review systematically examines the recent research from the past decade on diverse path-planning algorithms tailored for stereotactic neurosurgery applications. Our comprehensive investigation involved a thorough search of scholarly papers from Google Scholar, PubMed, IEEE Xplore, and Scopus, utilizing stringent inclusion and exclusion criteria. The screening and selection process was meticulously conducted by a multidisciplinary team comprising three medical students, robotic experts with specialized knowledge in path-planning techniques and medical robotics, and a board-certified neurosurgeon. Each selected paper was reviewed in detail, and the findings were synthesized and reported in this review. The paper is organized around three different types of intervention tools: straight needles, steerable needles, and concentric tube robots. We provide an in-depth analysis of various path-planning algorithms applicable to both single and multi-target scenarios. Multi-target planning techniques are only discussed for straight tools as there is no published work on multi-target planning for steerable needles and concentric tube robots. Additionally, we discuss the imaging modalities employed, the critical anatomical structures considered during path planning, and the current status of research regarding its translation to clinical human studies. To the best of our knowledge and as a conclusion from this systematic review, this is the first review paper published in the last decade that reports various path-planning techniques for different types of tools for minimally invasive neurosurgical applications. Furthermore, this review outlines future trends and identifies existing technology gaps within the field. By highlighting these aspects, we aim to provide a comprehensive overview that can guide future research and development in path planning for stereotactic neurosurgery, ultimately contributing to the advancement of safer and more effective neurosurgical procedures.

Planning System for the Optimization of Electric Field Delivery using Implanted Electrodes for Brain Tumor Control

BACKGROUND

The use of non-ionizing electric fields from low intensity voltage sources (<10 V) to control malignant tumor growth is showing increasing potential as a cancer treatment modality. A method of applying these low intensity electric fields using multiple implanted electrodes within or adjacent to tumor volumes has been termed as intratumoral modulation therapy (IMT).

PURPOSE

This study explores advancements in the previously established IMT optimization algorithm, and the development of a custom treatment planning system for patient specific IMT. The practicality of the treatment planning system is demonstrated by implementing the full optimization pipeline on a brain phantom with robotic electrode implantation, post-operative imaging, and treatment stimulation.

METHODS

The integrated planning pipeline in 3D Slicer begins with importing and segmenting patient magnetic resonance images (MRI) or computed tomography (CT) images. The segmentation process is manual, followed by a semi-automatic smoothing step that allows the segmented brain and tumor mesh volumes to be smoothed and simplified by applying selected filters. Electrode trajectories are planned manually on the patient MRI or CT by selecting insertion and tip coordinates for a chosen number of electrodes. The electrode tip positions, and stimulation parameters (phase shift and voltage) can then be optimized with the custom semi-automatic IMT optimization algorithm where users can select the prescription electric field, voltage amplitude limit, tissue electrical properties, nearby organs at risk, optimization parameters (electrode tip location, individual contact phase shift and voltage), desired field coverage percent, and field conformity optimization. Tables of optimization results are displayed, and the resulting electric field is visualized as a field-map superimposed on the MR or CT image, with 3D renderings of the brain, tumor, and electrodes. Optimized electrode coordinates are transferred to robotic electrode implantation software to enable planning and subsequent implantation of the electrodes at the desired trajectories.

RESULTS

An IMT treatment planning system was developed that incorporates patient specific MRI or CT, segmentation, volume smoothing, electrode trajectory planning, electrode tip location and stimulation parameter optimization, and results visualization. All previous manual pipeline steps operating on diverse software platforms were coalesced into a single semi-automated 3D Slicer based user interface. Brain phantom validation of the full system implementation was successful in pre-operative planning, robotic electrode implantation, and post-operative treatment planning to adjust stimulation parameters based on actual implant locations. Voltage measurements were obtained in the brain phantom to determine the electrical parameters of the phantom and validate the simulated electric field distribution.

CONCLUSIONS

A custom treatment planning and implantation system for IMT has been developed in this study, and validated on a phantom brain model, providing an essential step in advancing IMT technology towards future clinical safety and efficacy investigations. This article is protected by copyright. All rights reserved.

Novel Geometries for Stereotactic Localizers

INTRODUCTION

The N-localizer is generally utilized in a 3-panel or, more rarely, a 4-panel system for computing stereotactic positions. However, a stereotactic frame that incorporates a 2-panel (bipanel) N-localizer system with panels affixed to only the left and right sides of the frame offers several advantages: improved ergonomics to attach the panels, reduced claustrophobia for the patient, mitigation of posterior panel contact with imaging systems, and reduced complexity. A bipanel system that comprises two standard N-localizer panels yields only two three-dimensional (3D) coordinates, which are insufficient to solve for the stereotactic matrix without further information. While additional information to determine the stereotactic positions could include scalar distances from Digital Imaging and Communications in Medicine (DICOM) metadata or 3D regression across the imaging volume, both have risks related to noise and error propagation. Therefore, we sought to develop new stereotactic localizers that comprise only lateral fiducials (bipanel) that leave the front and back regions of the patient accessible but that contain enough information to solve for the stereotactic matrix using each image independently.  Methods: To solve the stereotactic matrix, we assumed the need to compute three or more 3D points from a single image. Several localizer options were studied using Monte Carlo simulations to understand the effect of errors on the computed target location. The simulations included millions of possible combinations for computing the stereotactic matrix in the presence of random errors of 1mm magnitude. The matrix then transformed coordinates for a target that was placed 50mm anterior, 50mm posterior, 50mm lateral, or 50mm anterior and 50mm lateral to the centre of the image. Simulated cross-sectional axial images of the novel localizer systems were created and converted into DICOM images representing computed tomography (CT) images.  Results: Three novel models include the M-localizer, F-localizer, and Z-localizer. For each of these localizer systems, optimized results were obtained using an overdetermined system of equations made possible by more than three diagonal bars. In each case, the diagonal bar position was computed using standard N-localizer mathematics. Additionally, the M-localizer allowed adding a computation using the Sturm-Pastyr method. Monte Carlo simulation demonstrated that the Z-localizer provided optimal results.

CONCLUSION

The three proposed novel models meet our design objectives. Of the three, the Z-localizer produced the least propagation of error. The M-localizer was simpler and had slightly more error than the Z-localizer. The F-localizer produced more error than either the Z-localizer or M-localizer. Further study is needed to determine optimizations using these novel models.

Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Reliable object tracking that is based on video data constitutes an important challenge in diverse areas, including, among others, assisted surgery. Particle filtering offers a state-of-the-art technology for this challenge. Becaise a particle filter is based on a probabilistic model, it provides explicit likelihood values; in theory, the question of whether an object is reliably tracked can be addressed based on these values, provided that the estimates are correct. In this contribution, we investigate the question of whether these likelihood values are suitable for deciding whether the tracked object has been lost. An immediate strategy uses a simple threshold value to reject settings with a likelihood that is too small. We show in an application from the medical domain-object tracking in assisted surgery in the domain of Robotic Osteotomies-that this simple threshold strategy does not provide a reliable reject option for object tracking, in particular if different settings are considered. However, it is possible to develop reliable and flexible machine learning models that predict a reject based on diverse quantities that are computed by the particle filter. Modeling the task in the form of a regression enables a flexible handling of different demands on the tracking accuracy; modeling the challenge as an ensemble of classification tasks yet surpasses the results, while offering the same flexibility.