Neuronavigation using susceptibility-weighted venography: application to deep brain stimulation and comparison with gadolinium contrast

Using virtual lines of navigation for a successful transcortical approach

Background

Neuronavigation systems have become essential tools in image-guided neurosurgery that aid in the accurate resection of brain tumors. Recent advancements to these devices can indicate the precise location of lesions but can also project an augmented reality (AR) image on the microscope eyepiece to facilitate a successful surgical operation. Although the transcortical approach is a very popular method in neurosurgery, it can lead to disorientation and can cause unnecessary brain damage when the distance from the brain surface to the lesion is long. Herein, we report on an actual case in which a virtual line from AR images was used to assist the transcortical approach.

Methods

A virtual line connecting the entry point and the target point, which were set as the navigation route, was created using Stealth station S7^® (Medtronic, Minneapolis, USA). This line was projected as an AR image on the microscope eyepiece. It was possible to reach the target point by proceeding through the white matter along the displayed virtual line.

Results

The lesion was reached within a short duration using virtual line without disorientation.

Conclusion

Setting a virtual line as an AR image using neuronavigation is a simple and accurate method that can effectively support the conventional transcortical approach.

Optimized Deep Brain Stimulation Surgery to Avoid Vascular Damage: A Single-Center Retrospective Analysis of Path Planning for Various Deep Targets by MRI Image Fusion

Co-registration of stereotactic and preoperative magnetic resonance imaging (MRI) images can serve as an alternative for trajectory planning. However, the role of this strategy has not yet been proven by any control studies, and the trajectories of commonly used targets have not been systematically studied. The purpose of this study was to analyze the trajectories for various targets, and to assess the role of trajectories realized on fused images in preventing intracranial hemorrhage (ICH). Data from 1019 patients who underwent electrode placement for deep brain stimulation were acquired. Electrode trajectories were not planned for 396 patients, whereas trajectories were planned for 623 patients. Preoperative various MRI sequences and frame-placed MRI images were fused for trajectory planning. The patients’ clinical characteristics, the stereotactic systems, intracranial hemorrhage cases, and trajectory angles were recorded and analyzed. No statistically significant differences in the proportions of male patients, patients receiving local anesthesia, and diseases or target distributions (p > 0.05) were found between the trajectory planning group and the non-trajectory planning group, but statistically significant differences were observed in the numbers of both patients and leads associated with symptomatic ICH (p < 0.05). Regarding the ring and arc angle values, statistically significant differences were found among various target groups (p < 0.05). The anatomic structures through which leads passed were found to be diverse. Trajectory planning based on MRI fusion is a safe technique for lead placement. The electrode for each given target has its own relatively constant trajectory.

Quantitative Analysis for the Delineation of the Subthalamic Nuclei on Three-Dimensional Stereotactic MRI Before Deep Brain Stimulation Surgery for Medication-Refractory Parkinson’s Disease

Delineation of the subthalamic nuclei (STN) on MRI is critical for deep brain stimulation (DBS) surgery in patients with Parkinson’s disease (PD). We propose this retrospective cohort study for quantitative analysis of MR signal-to-noise ratio (SNR), contrast, and signal difference-to-noise ratio (SDNR) of the STN on pre-operative three-dimensional (3D) stereotactic MRI in patients with medication-refractory PD. Forty-five consecutive patients with medication-refractory PD who underwent STN-DBS surgery in our hospital from January 2018 to June 2021 were included in this study. All patients had whole-brain 3D MRI, including T2-weighted imaging (T2WI), T2-weighted fluid-attenuated inversion recovery (FLAIR), and susceptibility-weighted imaging (SWI), at 3.0 T scanner for stereotactic navigation. The signal intensities of the STN, corona radiata, and background noise were obtained after placing regions of interest (ROIs) on corresponding structures. Quantitative comparisons of SNR, contrast, and SDNR of the STN between MR pulse sequences, including the T2WI, FLAIR, and SWI. Subgroup analysis regarding patients’ sex, age, and duration of treatment. We used one-way repeated measures analysis of variance for quantitative comparisons of SNR, contrast, and SDNR of the STN between different MR pulse sequences, and we also used the dependent t-test for the post hoc tests. In addition, we used Mann–Whitney U test for subgroup analyses. Both the contrast (0.33 ± 0.07) and SDNR (98.65 ± 51.37) were highest on FLAIR (all p < 0.001). The SNR was highest on SWI (276.16 ± 115.5), and both the SNR (94.23 ± 31.63) and SDNR (32.14 ± 17.23) were lowest on T2WI. Subgroup analyses demonstrated significantly lower SDNR on SWI for patients receiving medication treatment for ≥13 years (p = 0.003). In conclusion, on 3D stereotactic MRI of medication-refractory PD patients, the contrast and SDNR for the STN are highest on FLAIR, suggesting the optimal delineation of STN on FLAIR.

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.

Methodological Considerations for Neuroimaging in Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease Patients

Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson's disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians.

Experience Reduces Surgical and Hardware-Related Complications of Deep Brain Stimulation Surgery: A Single-Center Study of 181 Patients Operated in Six Years

Objective

Deep brain stimulation (DBS) surgery has increasingly been performed for the treatment of movement disorders and is associated with a wide array of complications. We aimed to present our experience and discuss strategies to minimize adverse events in light of this contemporary series and others in the literature.

Methods

A retrospective chart review was conducted to collect data on age, sex, indication, operation date, surgical technique, and perioperative and late complications.

Results

A total of 181 patients (113 males, 68 females) underwent DBS implantation surgery (359 leads) in the past six years. Indications and targets were as follows: Parkinson's disease (STN) (n=159), dystonia (GPi) (n=13), and essential tremor (Vim) (n=9). Mean age was 55.2 ± 11.7 (range 9-74) years. Mean follow-up duration was 3.4 ± 1.6 years. No mortality or permanent morbidity was observed. Major perioperative complications were confusion (6.6%), intracerebral hemorrhage (2.2%), stroke (1.1%), and seizures (1.1%). Long-term adverse events included wound (7.2%), mostly infection, and hardware-related (5.5%) complications. Among several factors, only surgical experience was found to be related with overall complication rates (early period: 31% versus late period: 10%; p=0.001).

Conclusion

The rates of both early and late complications of DBS surgery are acceptably low and decrease significantly with cumulative experience.

Laser Doppler sensing for blood vessel detection with a biologically inspired steerable needle

Puncturing blood vessels during percutaneous intervention in minimally invasive brain surgery can be a life threatening complication. Embedding a forward looking sensor in a rigid needle has been proposed to tackle this problem but, when using a rigid needle, the procedure needs to be interrupted and the needle extracted if a vessel is detected. As an alternative, we propose a novel optical method to detect a vessel in front of a steerable needle. The needle itself is based on a biomimetic, multi-segment design featuring four hollow working channels. Initially, a laser Doppler flowmetry probe is characterized in a tissue phantom with optical properties mimicking those of human gray matter. Experiments are performed to show that the probe has a 2.1 mm penetration depth and a 1 mm off-axis detection range for a blood vessel phantom with 5 mm s^-1 flow velocity. This outcome demonstrates that the probe fulfills the minimum requirements for it to be used in conjunction with our needle. A pair of Doppler probes is then embedded in two of the four working channels of the needle and vessel reconstruction is performed using successive measurements to determine the depth and the off-axis position of the vessel from each laser Doppler probe. The off-axis position from each Doppler probe is then used to generate a 'detection circle' per probe, and vessel orientation is predicted using tangent lines between the two. The vessel reconstruction has a depth root mean square error (RMSE) of 0.3 mm and an RMSE of 15° in the angular prediction, showing real promise for a future clinical application of this detection system.

Impact of Trajectory Planning With Susceptibility-Weighted Imaging for Intracranial Electrode Implantation

BACKGROUND

While T1-weighted gadolinium-enhanced (T1-Gd) magnetic resonance imaging (MRI) is the standard imaging sequence for trajectory planning of stereotactic procedures, including deep brain stimulation, stereoelectroencephalography, and laser interstitial thermal therapy, susceptibility-weighted imaging (SWI) has been reported to demonstrate increased sensitivity for the visualization of microvasculature.

OBJECTIVE

To determine the impact of SWI visualization on trajectory planning for electrode implantation and evaluate the relationship between the rate of vessel-electrode intersections and intracerebral hemorrhage (ICH).

METHODS

We conducted a retrospective study of 13 patients who underwent stereoelectroencephalography and laser interstitial thermal therapy placement between 2014 and 2015, using their preoperative T1-Gd and SWI scans, and postoperative MRI scans to determine the rate of vessel-electrode intersections seen on the 2 imaging modalities, the mean diameter and depth of the vessels identified, and the rate of ICH after implantation.

RESULTS

Among 13 patients, 106 electrodes were implanted. Sixty-three unique vessel-electrode intersections were identified on SWI with a mean of 4.85 intersections per patient. There were 13 intersections seen on T1-Gd with a mean of 1 intersection per patient. The intersected vessels visualized on SWI had a diameter of 1.49 ± 0.46 mm and those on T1-Gd were 2.01 ± 0.52 mm. There was no clear ICH observed in this series.

CONCLUSION

SWI allows for improved visualization of the smaller, deep vessels, whereas T1-Gd adequately detects superficial, larger vessels. Despite the larger number of vessel-electrode intersections seen on SWI, no clear evidence of ICH was identified. Increased detection of deep vasculature does not appear to significantly benefit trajectory planning for stereotactic intracranial procedures and may limit the number of trajectories perceived to be safe.

MAVEN: An Algorithm for Multi-Parametric Automated Segmentation of Brain Veins From Gradient Echo Acquisitions

Cerebral vein analysis provides a chance to study, from an unusual viewpoint, an entire class of brain diseases, including neurodegenerative disorders and traumatic brain injuries. Manual segmentation approaches can be used to assess vascular anatomy, but they are observer-dependent and time-consuming; therefore, automated approaches are desirable, as they also improve reproducibility. In this paper, a new, fully automated algorithm, based on structural, morphological, and relaxometric information, is proposed to segment the entire cerebral venous system from MR images. The algorithm for multi-parametric automated segmentation of brain VEiNs (MAVEN) is based on a combined investigation of multi-parametric information that allows for rejection of false positives and detection of thin vessels. The method is tested on gradient echo brain data sets acquired at 1.5, 3, and 7 T. It is compared to previous methods against manual segmentation, and its inter-scan reproducibility is assessed. The achieved accuracy and reproducibility are good, meaning that MAVEN outperforms previous methods on both quantitative and qualitative analyses. It is usable at all the field strengths explored, showing comparable accuracy scores, with no need for algorithm parameter adjustments, and thus, it is a promising candidate for the characterization of the venous tree topology.

Can We Rely on Susceptibility-Weighted Imaging for Subthalamic Nucleus Identification in Deep Brain Stimulation Surgery?

BACKGROUND

Susceptibility-weighted imaging (SWI) offers significantly improved visibility of the subthalamic nucleus (STN) compared with traditional T2-weighted imaging. However, it is unknown whether the representation of the nucleus on SWI corresponds to the neurophysiological location of the STN.

OBJECTIVE

To determine the correlation between the intraoperative electrophysiological activity of the STN and the representation of the nucleus on different magnetic resonance imaging (MRI) sequences used for deep brain stimulation target planning.

METHODS

At stereotactic target depth, microelectrode recordings (MERs) of typical STN neuronal activity were mapped on 3 different preoperative MRI sequences: 1.5-T SWI, 1.5-T T2-weighted, and 3-T T2-weighted MRI. For each MRI sequence, it was determined whether the MER signal was situated inside or outside the contour of the STN.

RESULTS

A total of 196 MER tracks in 34 patients were evaluated. In 165 tracks (84%), typical electrophysiological STN activity was measured. MER activity was situated more consistently inside hypointense STN contour representation on 1.5- and 3-T T2-weighted images compared with SWI (99% and 100% vs 79%, respectively). The 21% incongruence of electrophysiological STN activity outside the STN contour on SWI was seen almost exclusively in the anterior and lateral microelectrode channels.

CONCLUSION

STN representation on SWI does not correspond to electrophysiological STN borders. SWI does not correctly display the lateral part of the STN. When aiming to target the superolateral sensorimotor part of the STN during deep brain stimulation surgery, SWI does not offer an advantage but a disadvantage compared with conventional T2. Future research is needed to determine whether these findings may also apply for high-field SWI.

Brain shift in neuronavigation of brain tumors: A review

PURPOSE

Neuronavigation based on preoperative imaging data is a ubiquitous tool for image guidance in neurosurgery. However, it is rendered unreliable when brain shift invalidates the patient-to-image registration. Many investigators have tried to explain, quantify, and compensate for this phenomenon to allow extended use of neuronavigation systems for the duration of surgery. The purpose of this paper is to present an overview of the work that has been done investigating brain shift.

METHODS

A review of the literature dealing with the explanation, quantification and compensation of brain shift is presented. The review is based on a systematic search using relevant keywords and phrases in PubMed. The review is organized based on a developed taxonomy that classifies brain shift as occurring due to physical, surgical or biological factors.

RESULTS

This paper gives an overview of the work investigating, quantifying, and compensating for brain shift in neuronavigation while describing the successes, setbacks, and additional needs in the field. An analysis of the literature demonstrates a high variability in the methods used to quantify brain shift as well as a wide range in the measured magnitude of the brain shift, depending on the specifics of the intervention. The analysis indicates the need for additional research to be done in quantifying independent effects of brain shift in order for some of the state of the art compensation methods to become useful.

CONCLUSION

This review allows for a thorough understanding of the work investigating brain shift and introduces the needs for future avenues of investigation of the phenomenon.

Automatic SWI Venography Segmentation Using Conditional Random Fields

Susceptibility-weighted imaging (SWI) venography can produce detailed venous contrast and complement arterial dominated MR angiography (MRA) techniques. However, these dense reversed-contrast SWI venograms pose new segmentation challenges. We present an automatic method for whole-brain venous blood segmentation in SWI using Conditional Random Fields (CRF). The CRF model combines different first and second order potentials. First-order association potentials are modeled as the composite of an appearance potential, a Hessian-based shape potential and a non-linear location potential. Second-order interaction potentials are modeled using an auto-logistic (smoothing) potential and a data-dependent (edge) potential. Minimal post-processing is used for excluding voxels outside the brain parenchyma and visualizing the surface vessels. The CRF model is trained and validated using 30 SWI venograms acquired within a population of deep brain stimulation (DBS) patients (age range [Formula: see text] years). Results demonstrate robust and consistent segmentation in deep and sub-cortical regions (median kappa = 0.84 and 0.82), as well as in challenging mid-sagittal and surface regions (median kappa = 0.81 and 0.83) regions. Overall, this CRF model produces high-quality segmentation of SWI venous vasculature that finds applications in DBS for minimizing hemorrhagic risks and other surgical and non-surgical applications.