Accuracy, precision, and safety of stereotactic, frame-based, intraoperative MRI-guided and MRI-verified deep brain stimulation in 650 consecutive procedures

OBJECTIVE

Suboptimal lead placement is one of the most common indications for deep brain stimulation (DBS) revision procedures. Confirming lead placement in relation to the visible anatomical target with dedicated stereotactic imaging before terminating the procedure can mitigate this risk. In this study, the authors examined the accuracy, precision, and safety of intraoperative MRI (iMRI) to both guide and verify lead placement during frame-based stereotactic surgery.

METHODS

A retrospective analysis of 650 consecutive DBS procedures for targeting accuracy, precision, and perioperative complications was performed. Frame-based lead placement took place in an operating room equipped with an MRI machine using stereotactic images to verify lead placement before removing the stereotactic frame. Immediate lead relocation was performed when necessary. Systematic analysis of the targeting error was calculated.

RESULTS

Verification of 1201 DBS leads with stereotactic MRI was performed in 643 procedures and with stereotactic CT in 7. The mean ± SD of the final targeting error was 0.9 ± 0.3 mm (range 0.1-2.3 mm). Anatomically acceptable lead placement was achieved with a single brain pass for 97% (n = 1164) of leads; immediate intraoperative relocation was performed in 37 leads (3%) to obtain satisfactory anatomical placement. General anesthesia was used in 91% (n = 593) of the procedures. Hemorrhage was noted after 4 procedures (0.6%); 3 patients (0.4% of procedures) presented with transient neurological symptoms, and 1 experienced delayed cognitive decline. Two bleeds coincided with immediate relocation (2 of 37 leads, 5.4%), which contrasts with hemorrhage in 2 (0.2%) of 1164 leads implanted on the first pass (p = 0.0058). Three patients had transient seizures in the postoperative period. The seizures coincided with hemorrhage in 2 of these patients and with immediate lead relocation in the other. There were 21 infections (3.2% of procedures, 1.5% in 3 months) leading to hardware removal. Delayed (> 3 months) retargeting of 6 leads (0.5%) in 4 patients (0.6% of procedures) was performed because of suboptimal stimulation benefit. There were no MRI-related complications, no permanent motor deficits, and no deaths.

CONCLUSIONS

To the authors' knowledge, this is the largest series reporting the use of iMRI to guide and verify lead location during DBS surgery. It demonstrates a high level of accuracy, precision, and safety. Significantly higher hemorrhage was encountered when multiple brain passes were required for lead implantation, although none led to permanent deficit. Meticulous audit and calibration can improve precision and maximize safety.

Increased variance in second electrode accuracy during deep brain stimulation and its relationship to pneumocephalus, brain shift, and clinical outcomes: A retrospective cohort study

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Overall electrode accuracy was 0.22+/-0.4 ​mm with only 3 (4%) electrodes out with 2 ​mm from the intended target.

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Accuracy was significantly worse in the GPi versus the STN and on the second side implanted.

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Inaccuracy occurred in the X (lateral) plane but was not related to pneumocephalus or brain shift.

Intraoperative Computed Tomography for Registration of Stereotactic Frame in Frame-Based Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) electrode placement utilizing a frame-based technique requires registration of the stereotactic frame with computed tomography (CT) or magnetic resonance (MR) imaging. This traditionally has been accomplished with a conventional CT scanner. In recent years, intraoperative CT has become more prevalent.

OBJECTIVE

To compare the coordinates obtained with intraoperative CT and conventional CT for registration of the stereotactic frame for DBS.

METHODS

Patients undergoing DBS electrode placement between 2015 and 2017, who underwent both conventional and intraoperative CT for registration of the stereotactic frame, were included for analysis. The coordinates for the stereotactic target, anterior commissure, and posterior commissure for each CT method were recorded. The mean, maximum, minimum, and standard deviation of the absolute difference for each of the paired coordinates was calculated. Paired t-tests were performed to test for statistical significance of the difference. The directional difference as well as the vector error between the paired coordinates was also calculated.

RESULTS

The mean absolute difference between conventional and intraoperative CT for the coordinate pairs was less than 0.279 mm or 0.211 degrees for all coordinate pairs analyzed. This was not statistically significant for any of the coordinate pairs. Moreover, the maximum absolute difference between all coordinate pairs was 1.04 mm.

CONCLUSION

Intraoperative CT imaging provides stereotactic frame registration coordinates that are similar to those obtained by a standard CT scanner. This may save time and hospital resources by obviating the need for the patient to go to the radiology department for a CT scan.

A literature review of magnetic resonance imaging sequence advancements in visualizing functional neurosurgery targets

OBJECTIVE

Historically, preoperative planning for functional neurosurgery has depended on the indirect localization of target brain structures using visible anatomical landmarks. However, recent technological advances in neuroimaging have permitted marked improvements in MRI-based direct target visualization, allowing for refinement of "first-pass" targeting. The authors reviewed studies relating to direct MRI visualization of the most common functional neurosurgery targets (subthalamic nucleus, globus pallidus, and thalamus) and summarize sequence specifications for the various approaches described in this literature.

METHODS

The peer-reviewed literature on MRI visualization of the subthalamic nucleus, globus pallidus, and thalamus was obtained by searching MEDLINE. Publications examining direct MRI visualization of these deep brain stimulation targets were included for review.

RESULTS

A variety of specialized sequences and postprocessing methods for enhanced MRI visualization are in current use. These include susceptibility-based techniques such as quantitative susceptibility mapping, which exploit the amount of tissue iron in target structures, and white matter attenuated inversion recovery, which suppresses the signal from white matter to improve the distinction between gray matter nuclei. However, evidence confirming the superiority of these sequences over indirect targeting with respect to clinical outcome is sparse. Future targeting may utilize information about functional and structural networks, necessitating the use of resting-state functional MRI and diffusion-weighted imaging.

CONCLUSIONS

Specialized MRI sequences have enabled considerable improvement in the visualization of common deep brain stimulation targets. With further validation of their ability to improve clinical outcomes and advances in imaging techniques, direct visualization of targets may play an increasingly important role in preoperative planning.

SepaConvNet for Localizing the Subthalamic Nucleus Using One Second Micro-electrode Recordings

Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosurgical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.

Deep Learning-Based Deep Brain Stimulation Targeting and Clinical Applications

Background

The 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.

Methods

T2^∗ 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.

Results

Mean 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.

Conclusion

The 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.