Deep Brain Stimulation Planning Research Articles

New radiological techniques for planning of deep brain stimulation

Deep Brain Stimulation (DBS) has become widely accepted for treatment of many neurological disorders. Its success depends on several factors, of which imaging plays a crucial role for exact targeting of the deep nuclei. T2 weighted Magnetic Resonance Imaging (MRI) had still been used in routine practice for a long time. However, there are some new MRI sequences and techniques available which enhances direct targeting in planning of DBS surgeries. In this mini review, those recent developments were discussed briefly.

Comparison of two segmentation software tools for deep brain stimulation of the subthalamic and ventro-intermedius nuclei.

Deep brain stimulation (DBS) relies on precise targeting of key structures such as the subthalamic nucleus (STN) for Parkinson's disease (PD) and the ventro-intermedius nucleus of the thalamus (Vim) for essential tremor (ET). Segmentation software, such as GuideXT© and Suretune©, are commercially available for atlas-based identification of deep brain structures. However, no study has compared the concordance of the segmentation results between the two software. We retrospectively compared the concordance of segmentation of GuideXT© and Suretune© software by comparing the position of the segmented key structures with clinically predicted targets obtained using the newly developed RebrAIn© software as a reference. We targeted the STN in 44 MRI from PD patients (88 hemispheres) and the Vim in 31 MRI from ET patients (62 hemispheres) who were elected for DBS. In 22 STN targeting (25%), the target positioning was not correlating between GuideXT© and Suretune©. Regarding the Vim, targets were located in the segmented Vim in 37%, the posterior subthalamic area (PSA) in 60%, and the STN in 3% of the cases using GuideXT©; the proportions were 34%, 60%, and 6%, respectively, using Suretune©. The mean distance from the centre of the RebrAIn© targeting to the segmented Vim by Suretune© was closer (0.64mm) than with GuideXT© (0.96mm; p = 0.0004). While there is some level of concordance in the segmentation results of key structures for DBS treatment among software models, differences persist. Therefore, such software should still be considered as tools and should not replace clinician experience in DBS planning.

Comparison of Image Quality and Radiation Dose Between Single-Energy and Dual-Energy Images for the Brain With Stereotactic Frames on Dual-Energy Cerebral CT.

Preoperative stereotactic planning of deep brain stimulation (DBS) using computed tomography (CT) imaging in patients with Parkinson's disease (PD) is of clinical interest. However, frame-induced metal artifacts are common in clinical practice, which can be challenging for neurosurgeons to visualize brain structures. To evaluate the image quality and radiation exposure of patients with stereotactic frame brain CT acquired using a dual-source CT (DSCT) system in single- and dual-energy modes. We included 60 consecutive patients with Parkinson's disease (PD) and randomized them into two groups. CT images of the brain were performed using DSCT (Group A, an 80/Sn150 kVp dual-energy mode; Group B, a 120 kVp single-energy mode). One set of single-energy images (120 kVp) and 10 sets of virtual monochromatic images (50-140 keV) were obtained. Subjective image analysis of overall image quality was performed using a five-point Likert scale. For objective image quality evaluation, CT values, image noise, signal-to-noise ratio (SNR), and contrast-to-noise (CNR) were calculated. The radiation dose was recorded for each patient. The mean effective radiation dose was reduced in the dual-energy mode (1.73 mSv ± 0.45 mSv) compared to the single-energy mode (3.16 mSv ± 0.64 mSv) (p < 0.001). Image noise was reduced by 46-52% for 120-140 keV VMI compared to 120 kVp images (both p < 0.01). CT values were higher at 100-140 keV than at 120 kVp images. At 120-140 keV, CT values of brain tissue showed significant differences at the level of the most severe metal artifacts (all p < 0.05). SNR was also higher in the dual-energy mode 90-140 keV compared to 120 kVp images, showing a significant difference between the two groups at 120-140 keV (all p < 0.01). The CNR was significantly better in Group A for 60-140 keV VMI compared to Group B (both p < 0.001). The highest subjective image scores were found in the 120 keV images, while 110-140 keV images had significantly higher scores than 120 kVp images (all p < 0.05). DSCT images using dual-energy modes provide better objective and subjective image quality for patients with PD at lower radiation doses compared to single-energy modes and facilitate brain tissue visualization with stereotactic frame DBS procedures.

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization.

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.

A novel prototype for virtual-reality-based deep brain stimulation trajectory planning using voodoo doll annotation and eye-tracking

ABSTRACT Deep brain stimulation (DBS) is an effective surgical treatment for Parkinson’s disease. The procedure requires precise placement of a stimulation electrode into the therapeutic target while avoiding vital anatomies (e.g. blood vessels) to prevent surgical risks. Therefore, multi-contrast imaging data are often employed to capture full anatomical details for electrode trajectory planning. However, with multiple constraints to consider from several image contrasts, surgical planning with conventional 2D-display-based neuro-navigation software can be time-consuming and challenging. Virtual reality (VR) allows intuitive interaction with 3D data, and thus is an excellent fit to navigate complex anatomy for neurosurgical planning. We present the first VR-based DBS trajectory planning system, where we used a novel voodoo doll interaction strategy to allow precise surgical target selection and a line-of-sight approach with eye-tracking to determine optimal DBS trajectories. With preliminary user studies, the proposed system demonstrates great promises for efficient and intuitive DBS planning.

New Frontiers for Deep Brain Stimulation: Directionality, Sensing Technologies, Remote Programming, Robotic Stereotactic Assistance, Asleep Procedures, and Connectomics.

Over the last few years, while expanding its clinical indications from movement disorders to epilepsy and psychiatry, the field of deep brain stimulation (DBS) has seen significant innovations. Hardware developments have introduced directional leads to stimulate specific brain targets and sensing electrodes to determine optimal settings via feedback from local field potentials. In addition, variable-frequency stimulation and asynchronous high-frequency pulse trains have introduced new programming paradigms to efficiently desynchronize pathological neural circuitry and regulate dysfunctional brain networks not responsive to conventional settings. Overall, these innovations have provided clinicians with more anatomically accurate programming and closed-looped feedback to identify optimal strategies for neuromodulation. Simultaneously, software developments have simplified programming algorithms, introduced platforms for DBS remote management via telemedicine, and tools for estimating the volume of tissue activated within and outside the DBS targets. Finally, the surgical accuracy has improved thanks to intraoperative magnetic resonance or computerized tomography guidance, network-based imaging for DBS planning and targeting, and robotic-assisted surgery for ultra-accurate, millimetric lead placement. These technological and imaging advances have collectively optimized DBS outcomes and allowed “asleep” DBS procedures. Still, the short- and long-term outcomes of different implantable devices, surgical techniques, and asleep vs. awake procedures remain to be clarified. This expert review summarizes and critically discusses these recent innovations and their potential impact on the DBS field.

Visualization of volume of tissue activated modeling in a clinical planning system for deep brain stimulation.

Pathway activating models try to describe stimulation spread in deep brain stimulation (DBS). Volume of tissue activated (VTA) models are simplified model variants allowing faster and easier computation. Our study aimed to investigate, how VTA visualization can be integrated into a clinical workflow applying directional electrodes using a standard clinical DBS planning system. Twelve patients underwent DBS, using directional electrodes for bilateral subthalamic nucleus (STN) stimulation in Parkinson's disease. Preoperative 3T magnetic resonance imaging was used for automatic visualization of the STN outline, as well as for fiber tractography. Intraoperative computed tomography was used for automatic lead detection. The Guide XT software, closely integrated into the DBS planning software environment, was used for VTA calculation and visualization. VTA visualization was possible in all cases. The percentage of VTA covering the STN volume ranged from 25% to 100% (mean: 60±25%) on the left side and from 0% to 98% (51±30%) on the right side. The mean coordinate of all VTA centers was: 12.6±1.2 mm lateral, 2.1±1.2 mm posterior, and 2.3±1.4 mm inferior in relation to the midcommissural point. Stimulation effects can be compared to the VTA visualization in relation to surrounding structures, potentially facilitating programming, which might be especially beneficial in case of suboptimal lead placement. VTA visualization in a clinical planning system allows an intuitive adjustment of the stimulation parameters, supports programming, and enhances understanding of effects and side effects of DBS.

Spatial versus angular resolution for tractography-assisted planning of deep brain stimulation.

Given the restricted total scanning time for clinical neuroimaging, it is unclear whether clinical diffusion MRI protocols would benefit more from higher spatial resolution or higher angular resolution. In this work, we investigated the relative benefit of improving spatial or angular resolution in diffusion MRI to separate two parallel running white matter tracts that are targets for deep brain stimulation: the anterior thalamic radiation and the supero-lateral branch of the medial forebrain bundle. Both these tracts are situated in the ventral anterior limb of the internal capsule, and recent studies suggest that targeting a specific tract could improve treatment efficacy. Therefore, we scanned 19 healthy volunteers at 3T and 7T according to three diffusion MRI protocols with respectively standard clinical settings, increased spatial resolution of 1.4mm, and increased angular resolution (64 additional gradient directions at b=2200s/mm2). We performed probabilistic tractography for all protocols and quantified the separability of both tracts. The higher spatial resolution protocol improved separability by 41% with respect to the clinical standard, presumably due to decreased partial voluming. The higher angular resolution protocol resulted in increased apparent tract volumes and overlap, which is disadvantageous for application in precise treatment planning. We thus recommend to increase the spatial resolution for deep brain stimulation planning to 1.4mm while maintaining angular resolution. This recommendation complements the general advice to aim for high angular resolution to resolve crossing fibers, confirming that the specific application and anatomical considerations are leading in clinical diffusion MRI protocol optimization.

Deep Learning-Based Deep Brain Stimulation Targeting and Clinical Applications.

BackgroundThe purpose of the present study was to evaluate deep learning-based image-guided surgical planning for deep brain stimulation (DBS). We developed deep learning semantic segmentation-based DBS targeting and prospectively applied the method clinically.MethodsT2∗ fast gradient-echo images from 102 patients were used for training and validation. Manually drawn ground truth information was prepared for the subthalamic and red nuclei with an axial cut ∼4 mm below the anterior–posterior commissure line. A fully convolutional neural network (FCN-VGG-16) was used to ensure margin identification by semantic segmentation. Image contrast augmentation was performed nine times. Up to 102 original images and 918 augmented images were used for training and validation. The accuracy of semantic segmentation was measured in terms of mean accuracy and mean intersection over the union. Targets were calculated based on their relative distance from these segmented anatomical structures considering the Bejjani target.ResultsMean accuracies and mean intersection over the union values were high: 0.904 and 0.813, respectively, for the 62 training images, and 0.911 and 0.821, respectively, for the 558 augmented training images when 360 augmented validation images were used. The Dice coefficient converted from the intersection over the union was 0.902 when 720 training and 198 validation images were used. Semantic segmentation was adaptive to high anatomical variations in size, shape, and asymmetry. For clinical application, two patients were assessed: one with essential tremor and another with bradykinesia and gait disturbance due to Parkinson’s disease. Both improved without complications after surgery, and microelectrode recordings showed subthalamic nuclei signals in the latter patient.ConclusionThe accuracy of deep learning-based semantic segmentation may surpass that of previous methods. DBS targeting and its clinical application were made possible using accurate deep learning-based semantic segmentation, which is adaptive to anatomical variations.

Machine learning\u2014aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET

BackgroundGrowing interest exists for superolateral medial forebrain bundle (slMFB) deep brain stimulation (DBS) in psychiatric disorders. The surgical approach warrants tractographic rendition. Commercial stereotactic planning systems use deterministic tractography which suffers from inherent limitations, is dependent on manual interaction (ROI definition), and has to be regarded as subjective. We aimed to develop an objective but patient-specific tracking of the slMFB which at the same time allows the use of a commercial surgical planning system in the context of deep brain stimulation.MethodsThe HAMLET (Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI) machine learning approach was introduced into the standardized workflow of slMFB DBS tractographic planning on the basis of patient-specific dMRI. Rendition of the slMFB with HAMLET serves as an objective comparison for the refinement of the deterministic tracking procedure. Our application focuses on the tractographic planning of DBS (N = 8) for major depression and OCD.ResultsPrevious results have shown that only fibers belonging to the ventral tegmental area to prefrontal/orbitofrontal axis should be targeted. With the proposed technique, the deterministic tracking approach, that serves as the surgical planning data, can be refined, over-sprouting fibers are eliminated, bundle thickness is reduced in the target region, and thereby probably a more accurate targeting is facilitated. The HAMLET-driven method is meant to achieve a more objective surgical fiber display of the slMFB with deterministic tractography.ConclusionsThe approach allows overlying the results of patient-specific planning from two different approaches (manual deterministic and machine learning HAMLET). HAMLET shows the slMFB as a volume and thus serves as an objective tracking corridor. It helps to refine results from deterministic tracking in the surgical workspace without interfering with any part of the standard software solution. We have now included this workflow in our daily clinical experimental work on slMFB DBS for psychiatric indications.

Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2 W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

Combination of CT angiography and MRI in surgical planning of deep brain stimulation.

For safe deep brain stimulation (DBS) planning, an accurate visualization and localization of vessels is mandatory. Contrast enhanced (ce) MRI depicts both arteries and veins. Computed tomography angiography (CTA) detects arteries with high geometric accuracy. We routinely combine both modalities for DBS planning. A total of 222 trajectories in a consecutive series of 113 patients who underwent DBS operations were included. In all trajectories, the number of veins and arteries in a 10-mm diameter around the planned trajectory were counted in a ceMRI and a CTA. If a vessel was visible in both modalities, the distance was measured. A total of 370 vessels were counted. Two hundred forty vessels (65%) were visible in both modalities. With 134 of the vessels, we detected a difference of the vessel's location with an average distance of 1.24mm (SD 0.58). Eighty vessels (22%) were visible only in the ceMRI, 50 vessels (13%) only in the CTA. We had four bleedings (1.8% per lead) of which one was symptomatic (0.45%). The majority of vessels were visible in both modalities; however, in more than half of these cases, the location was not identical. Here, the location in the CTA can be regarded as the ground truth. Moreover, both the CTA and the ceMRI depicted vessels not seen in the other imaging modality. We therefore assume that the combination of both imaging modalities for DBS planning increases the chance to detect vascular conflicts along the trajectory, thus reducing the risk of intracranial bleeding.

Self-guided training for deep brain stimulation planning using objective assessment.

Deep brain stimulation (DBS) is an increasingly common treatment for neurodegenerative diseases. Neurosurgeons must have thorough procedural, anatomical, and functional knowledge to plan electrode trajectories and thus ensure treatment efficacy and patient safety. Developing this knowledge requires extensive training. We propose a training approach with objective assessment of neurosurgeon proficiency in DBS planning. To assess proficiency, we propose analyzing both the viability of the planned trajectory and the manner in which the operator arrived at the trajectory. To improve understanding, we suggest a self-guided training course for DBS planning using real-time feedback. To validate the proposed measures of proficiency and training course, two experts and six novices followed the training course, and we monitored their proficiency measures throughout. At baseline, experts planned higher quality trajectories and did so more efficiently. As novices progressed through the training course, their proficiency measures increased significantly, trending toward expert measures. We developed and validated measures which reliably discriminate proficiency levels. These measures are integrated into a training course, which quantitatively improves trainee performance. The proposed training course can be used to improve trainees' proficiency, and the quantitative measures allow trainees' progress to be monitored.

Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated.

Deep brain stimulation (DBS) is a procedure requiring accurate targeting and electrode placement. The two key elements for successful planning are preserving patient safety by ensuring a safe trajectory and creating treatment efficacy through optimal selection of the stimulation point. In this work, we present the first approach of computer-assisted preoperative DBS planning to automatically optimize both the safety of the electrode's trajectory and location of the stimulation point so as to provide the best clinical outcome. Building upon the findings of previous works focused on electrode trajectory, we added a set of constraints guiding the choice of stimulation point. These took into account retrospective data represented by anatomo-clinical atlases and intersections between the stimulation region and sensitive anatomical structures causing side effects. We implemented our method into automatic preoperative planning software to assess if the algorithm was able to simultaneously optimize electrode trajectory and the stimulation point. Leave-one-out cross-validation on a dataset of 18 cases demonstrated an improvement in the expected outcome when using the new constraints. The distance to critical structures was not reduced. The intersection between the stimulation region and structures sensitive to stimulation was minimized. Introducing these new constraints guided the planning to select locations showing a trend toward symptom improvement, while minimizing the risks of side effects, and there was no cost in terms of trajectory safety.

Improving Surgical Outcome Using Diffusion Tensor Imaging Techniques in Deep Brain Stimulation.

Recent advances in surgical imaging include the use of diffusion tensor imaging (DTI) in deep brain stimulation (DBS) and provide a detailed view of the white matter tracts and their connections which are not seen with conventional magnetic resonance imaging. Given that the efficacy of DBS depends on the precise and accurate targeting of these circuits, better surgical planning using information obtained from DTI may lead to improved surgical outcome. We aim to review the available literature to evaluate the efficacy of such a strategy. A search of PubMed was performed to identify all articles using the search terms "(diffusion tractography OR diffusion tensor imaging OR DTI) AND (deep brain stimulation OR DBS)." Studies were included if DTI was used and clinical outcomes were reported. We identified 35 studies where the use of DTI in DBS was evaluated. The most studied pathology was movement disorders (17 studies), psychiatric disorders (11 studies), and pain (7 studies). The overall responder rates for tremor reduction was 70.0% (SD = 26.1%) in 69 patients, 36.5% (SD = 19.1%) for obsessive-compulsive disorder in 9 patients, 48.3% (SD = 40.0%) for depression in 40 patients, and 49.7% (SD = 35.1%) for chronic pain in 23 patients. The studies reviewed show that the use of DTI for surgical planning is feasible, provide additional information over conventional targeting methods, and can improve surgical outcome. Patients in whom the DBS electrodes were within the DTI targets experienced better outcomes than those in whom the electrodes were not. Many current studies are limited by their small sample size or retrospective nature. The use of DTI in DBS planning appears underutilized and further studies are warranted given that surgical outcome can be optimized using this non-invasive technique.

Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.

Recent advances in neuroimaging data acquisition and analysis hold the promise to enhance the ability to make diagnostic and prognostic predictions and perform treatment planning in neuropsychiatric disorders. Prior research using a variety of types of neuroimaging techniques has confirmed that neuropsychiatric disorders are associated with dysfunction in anatomical and functional brain circuits. We first discuss current challenges associated with the identification of reliable neuroimaging markers for diagnosis and prognosis in mood disorders and for neurosurgical treatment planning for deep brain stimulation (DBS). We then present data on the use of neuroimaging for the diagnosis and prognosis of mood disorders and for DBS treatment planning. We demonstrate how multivariate analyses of functional activation and connectivity parameters can be used to differentiate patients with bipolar disorder from those with major depressive disorder and non-affective psychosis. We also present data on connectivity parameters that mediate acute treatment response in affective and non-affective psychosis. We then focus on precision mapping of functional connectivity in native space. We describe the benefits of integrating anatomical fiber reconstruction with brain functional parameters and cortical surface measures to derive anatomically informed connectivity metrics within the morphological context of each individual brain. We discuss how this approach may be particularly promising in psychiatry, given the clinical and etiological heterogeneity of the disorders, and particularly in treatment response prediction and planning. Precision mapping of connectivity is essential for DBS. In DBS, treatment electrodes are inserted into positions near key gray matter nodes within the circuits considered relevant to disease expression. However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment. We demonstrate how this approach can be validated in the treatment of Parkinson’s disease by identifying connectivity patterns that can be used as biomarkers for treatment planning and thus refine the traditional approach of DBS planning that uses only gray matter landmarks. Finally, we describe how this approach could be used in planning DBS treatment of psychiatric disorders.

Electrophysiologic Validation of Diffusion Tensor Imaging Tractography during Deep Brain Stimulation Surgery.

Diffusion tensor imaging fiber tractography-assisted planning of deep brain stimulation is an emerging technology. We investigated its accuracy by using electrophysiology under clinical conditions. We hypothesized that a level of concordance between electrophysiology and DTI fiber tractography can be reached, comparable with published modeling approaches for deep brain stimulation surgery. Eleven patients underwent subthalamic nucleus deep brain stimulation. DTI scans and high-resolution T1- and T2-weighted MR imaging was performed at 3T. Corticospinal tracts were traced. We studied electrode positions and current amplitudes that elicited corticospinal tract effects during the operation to determine relative corticospinal tract distance. Postoperatively, 3D deep brain stimulation electrode contact locations and stimulation patterns were applied for the same corticospinal tract distance estimation. Intraoperative electrophysiologic (n = 40) clinical effects in 11 patients were detected. The mean intraoperative electrophysiologic corticospinal tract distance was 3.0 ± 0.6 mm; the mean image-derived corticospinal tract distance (DTI fiber tractography) was 3.0 ± 1.3 mm. The 95% limits of agreement were ±2.4 mm. Postoperative electrophysiology (n = 44) corticospinal tract activation effects were encountered in 9 patients; 39 were further evaluated. Mean electrophysiologic corticospinal tract distance was 3.7 ± 0.7 mm; for DTI fiber tractography, it was 3.2 ± 1.9 mm. The 95% limits of agreement were ±2.5 mm. DTI fiber tractography depicted the medial corticospinal tract border with proved concordance. Although the overall range of measurements was relatively small and variance was high, we believe that further use of DTI fiber tractography to assist deep brain stimulation procedures is advisable if inherent limitations are respected. These results confirm our previously published electric field simulation studies.

Magnetic resonance susceptibility weighted imaging in neurosurgery: current applications and future perspectives.

Susceptibility weighted imaging (SWI) is a relatively new imaging technique. Its high sensitivity to hemorrhagic components and ability to depict microvasculature by means of susceptibility effects within the veins allow for the accurate detection, grading, and monitoring of brain tumors. This imaging modality can also detect changes in blood flow to monitor stroke recovery and reveal specific subtypes of vascular malformations. In addition, small punctate lesions can be demonstrated with SWI, suggesting diffuse axonal injury, and the location of these lesions can help predict neurological outcome in patients. This imaging technique is also beneficial for applications in functional neurosurgery given its ability to clearly depict and differentiate deep midbrain nuclei and close submillimeter veins, both of which are necessary for presurgical planning of deep brain stimulation. By exploiting the magnetic susceptibilities of substances within the body, such as deoxyhemoglobin, calcium, and iron, SWI can clearly visualize the vasculature and hemorrhagic components even without the use of contrast agents. The high sensitivity of SWI relative to other imaging techniques in showing tumor vasculature and microhemorrhages suggests that it is an effective imaging modality that provides additional information not shown using conventional MRI. Despite SWI's clinical advantages, its implementation in MRI protocols is still far from consistent in clinical usage. To develop a deeper appreciation for SWI, the authors here review the clinical applications in 4 major fields of neurosurgery: neurooncology, vascular neurosurgery, neurotraumatology, and functional neurosurgery. Finally, they address the limitations of and future perspectives on SWI in neurosurgery.

Susceptibility-Weighted MRI for Deep Brain Stimulation: Potentials in Trajectory Planning

Background: Deep brain stimulation (DBS) trajectory planning is mostly based on standard 3-D T1-weighted gadolinium-enhanced MRI sequences (T1-Gd). Susceptibility-weighted MRI sequences (SWI) show neurovascular structures without the use of contrast agents. The aim of this study was to investigate whether SWI might be useful in DBS trajectory planning. Methods: We performed bilateral DBS planning using conventional T1-Gd images of 10 patients with different kinds of movement disorders. Afterwards, we matched SWI sequences and compared the visibility of vascular structures in both imaging modalities. Results: By analyzing 100 possible trajectories, we found a potential vascular conflict in 13 trajectories based on T1-Gd in contrast to 53 in SWI. Remarkably, all vessels visible in T1-Gd were also depicted in SWI, whereas SWI showed many additional vascular structures which could not be identified in T1-Gd. Conclusion/Discussion: The sensitivity for detecting neurovascular structures for DBS planning seems to be significantly higher in SWI. As SWI does not require a contrast agent, we suggest that SWI may be a valuable alternative to T1-Gd MRI for DBS trajectory planning. Furthermore, the data analysis suggests that vascular interactions of DBS trajectories might be more frequent than expected from the very low incidence of symptomatic bleedings. The explanation for this is currently the subject of debate and merits further studies.

Visualization of small veins with Susceptibility-Weighted Imaging for stereotactic trajectory planning in Deep Brain Stimulation

Intracerebral hemorrhage (ICH) is the most significant complication of Deep Brain Stimulation (DBS). To prevent ICH, stereotactic contrast enhanced T1-weighted images are used to visualize vessels as source of hemorrhage. Susceptibility-Weighted Imaging (SWI) is an MRI sequence with improved visualization of susceptibility differences between tissues, particularly sensitive for brain veins. The aim of this prospective study was to analyze the utility of SWI compared to contrast enhanced stereotactic T1-weighted images for trajectory planning of DBS. Preoperative SWI was performed in 33 patients undergoing DBS and was compared to the T1-weighted images. Vessels identified only with SWI in relation to the bilateral planned trajectory were analyzed. In all patients vessels were depicted on SWI only within the planned trajectory (range 1–4 vessels, for each trajectory, mean: 2.4). In 6 patients vessels were identified on SWI adjacent to the target (up to 5mm distal from target). In 11 patients SWI visualized additional cortical veins adjacent to the entry point of the trajectory. The apparent diameter of these vessels ranged between 0.8 and 2.1mm (mean: 1.2mm). Postoperative MRI was compared with preoperative SWI and revealed in two patients small (<3mm) T2 hyperintense lesions along electrodes without correlation with visualized veins. SWI facilitates the visualization of small veins superior to T1-weighted images. However, cerebral veins within the trajectory were not found to be a significant source of ICH after DBS. Potential sources of ICH are mesencephal veins at the endpoint of electrodes which can cause fatal hemorrhage and are visualized with SWI reliably.