Image Updating for Brain Shift Compensation During Resection

Model-based image updating in deep brain stimulation with assimilation of deep brain sparse data

BACKGROUND

Accuracy of electrode placement for deep brain stimulation (DBS) is critical to achieving desired surgical outcomes and impacts the efficacy of treating neurodegenerative diseases. Intraoperative brain shift degrades the accuracy of surgical navigation based on preoperative images.

PURPOSE

We extended a model-based image updating scheme to address intraoperative brain shift in DBS surgery and improved its accuracy in deep brain.

METHODS

We evaluated 10 patients, retrospectively, who underwent bilateral DBS surgery and classified them into groups of large and small deformation based on a 2 mm subsurface movement threshold and brain shift index of 5%. In each case, sparse brain deformation data were used to estimate whole brain displacements and deform preoperative CT (preCT) to generate updated CT (uCT). Accuracy of uCT was assessed using target registration errors (TREs) at the Anterior Commissure (AC), Posterior Commissure (PC), and four calcification points in the sub-ventricular area by comparing their locations in uCT with their ground truth counterparts in postoperative CT (postCT).

RESULTS

In the large deformation group, TREs were reduced from 2.5 mm in preCT to 1.2 mm in uCT (53% compensation); in the small deformation group, errors were reduced from 1.25 to 0.74 mm (41%). Average reduction of TREs at AC, PC and pineal gland were significant, statistically (p ⩽ 0.01).

CONCLUSIONS

With more rigorous validation of model results, this study confirms the feasibility of improving the accuracy of model-based image updating in compensating for intraoperative brain shift during DBS procedures by assimilating deep brain sparse data.

Precise Brain-shift Prediction by New Combination of W-Net Deep Learning for Neurosurgical Navigation

Brain tissue deformation during surgery significantly reduces the accuracy of image-guided neurosurgeries. We generated updated magnetic resonance images (uMR) in this study to compensate for brain shifts after dural opening using a convolutional neural network (CNN). This study included 248 consecutive patients who underwent craniotomy for initial intra-axial brain tumor removal and correspondingly underwent preoperative MR (pMR) and intraoperative MR (iMR) imaging. Deep learning using CNN to compensate for brain shift was performed using the pMR as input data, and iMR obtained after dural opening as the ground truth. For the tumor center (TC) and the maximum shift position (MSP), statistical analysis using the Wilcoxon signed-rank test was performed between the target registration error (TRE) for the pMR and iMR (i.e., the actual amount of brain shift) and the TRE for the uMR and iMR (i.e., residual error after compensation). The TRE at the TC decreased from 4.14 ± 2.31 mm to 2.31 ± 1.15 mm, and the TRE at the MSP decreased from 9.61 ± 3.16 mm to 3.71 ± 1.98 mm. The Wilcoxon signed-rank test of the pMR TRE and uMR TRE yielded a p-value less than 0.0001 for both the TC and MSP. Using a CNN model, we designed and implemented a new system that compensated for brain shifts after dural opening. Learning pMR and iMR with a CNN demonstrated the possibility of correcting the brain shift after dural opening.

Variables associated with cortical motor mapping thresholds: A retrospective data review with a unique case of interlimb motor facilitation

Introduction

Intraoperative neuromonitoring (IONM) is crucial to preserve eloquent neurological functions during brain tumor resections. We observed a rare interlimb cortical motor facilitation phenomenon in a patient with recurrent high-grade glioma undergoing craniotomy for tumor resection; the patient's upper arm motor evoked potentials (MEPs) increased in amplitude significantly (up to 44.52 times larger, p < 0.001) following stimulation of the ipsilateral posterior tibial nerve at 2.79 Hz. With the facilitation effect, the cortical MEP stimulation threshold was reduced by 6 mA to maintain appropriate continuous motor monitoring. It likely has the benefit of reducing the occurrence of stimulation-induced seizures and other adverse events associated with excessive stimulation.

Methods

We conducted a retrospective data review including 120 patients who underwent brain tumor resection with IONM at our center from 2018 to 2022. A broad range of variables collected pre-and intraoperatively were reviewed. The review aimed to determine: (1) whether we overlooked this facilitation phenomenon in the past, (2) whether this unique finding is related to any specific demographic information, clinical presentation, stimulation parameter (s) or anesthesia management, and (3) whether it is necessary to develop new techniques (such as facilitation methods) to reduce cortical stimulation intensity during intraoperative functional mapping.

Results

There is no evidence suggesting that clinical presentation, stimulation configuration, or intraoperative anesthesia management of the patient with the facilitation effect were significantly different from our general patient cohort. Even though we did not identify the same facilitation effect in any of these patients, we were able to determine that stimulation thresholds for motor mapping are significantly associated with the location of stimulation (p = 0.003) and the burst suppression ratio (BSR) (p < 0.001). Stimulation-induced seizures, although infrequent (4.05%), could occur unexpectedly even when the BSR was 70%.

Discussion

We postulated that functional reorganization and neuronal hyperexcitability induced by glioma progression and repeated surgeries were probable underlying mechanisms of the interlimb facilitation phenomenon. Our retrospective review also provided a practical guide to cortical motor mapping in brain tumor patients under general anesthesia. We also underscored the need for developing new techniques to reduce the stimulation intensity and, hence, seizure occurrence.

Tracking motor and language eloquent white matter pathways with intraoperative fiber tracking versus preoperative tractography adjusted by intraoperative MRI-based elastic fusion

OBJECTIVE

Preoperative fiber tracking (FT) enables visualization of white matter pathways. However, the intraoperative accuracy of preoperative image registration is reduced due to brain shift. Intraoperative FT is currently considered the standard of anatomical accuracy, while intraoperative imaging can also be used to correct and update preoperative data by intraoperative MRI (ioMRI)-based elastic fusion (IBEF). However, the use of intraoperative tractography is restricted due to the need for additional acquisition of diffusion imaging in addition to scanner limitations, quality factors, and setup time. Since IBEF enables compensation for brain shift and updating of preoperative FT, the aim of this study was to compare intraoperative FT with IBEF of preoperative FT.

METHODS

Preoperative MRI (pMRI) and ioMRI, both including diffusion tensor imaging (DTI) data, were acquired between February and November 2018. Anatomy-based DTI FT of the corticospinal tract (CST) and the arcuate fascicle (AF) was reconstructed at various fractional anisotropy (FA) values on pMRI and ioMRI, respectively. The intraoperative DTI FT, as a baseline tractography, was fused with original preoperative FT and IBEF-compensated FT, processes referred to as rigid fusion (RF) and elastic fusion (EF), respectively. The spatial overlap index (Dice coefficient [DICE]) and distances of surface points (average surface distance [ASD]) of fused FT before and after IBEF were analyzed and compared in operated and nonoperated hemispheres.

RESULTS

Seventeen patients with supratentorial brain tumors were analyzed. On the operated hemisphere, the overlap index of pre- and intraoperative FT of the CST by DICE significantly increased by 0.09 maximally after IBEF. A significant decrease by 0.5 mm maximally in the fused FT presented by ASD was observed. Similar improvements were found in IBEF-compensated FT, for which AF tractography on the tumor hemispheres increased by 0.03 maximally in DICE and decreased by 1.0 mm in ASD.

CONCLUSIONS

Preoperative tractography after IBEF is comparable to intraoperative tractography and can be a reliable alternative to intraoperative FT.

Intraoperative use of heads-up display in skull base surgery

In this video, the authors highlight the applications of virtual reality and heads-up display in skull base surgery by presenting the case of a 45-year-old woman with an incidental large clinoid meningioma extending into the posterior fossa. The patient underwent preoperative endovascular tumor embolization to facilitate tumor resection and reduce blood loss, followed by a right pterional craniotomy. The use of intraoperative Doppler, intraoperative neurophysiological monitoring, and endoscope-assisted microsurgery is also featured. A subtotal resection was planned given tumor encasement of the posterior communicating and anterior choroidal arteries. No new neurological deficits were noted after the surgical procedure.

The video can be found here: https://stream.cadmore.media/r10.3171/2021.10.FOCVID21177

Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data

Brain-shift during neurosurgery compromises the accuracy of tracking the boundaries of the tumor to be resected. Although several studies have used various finite element models (FEMs) to predict inward brain-shift, evaluation of their accuracy and efficiency based on public benchmark data has been limited. This study evaluates several FEMs proposed in the literature (various boundary conditions, mesh sizes, and material properties) by using intraoperative imaging data (the public REtroSpective Evaluation of Cerebral Tumors [RESECT] database). Four patients with low-grade gliomas were identified as having inward brain-shifts. We computed the accuracy (using target registration error) of several FEM-based brain-shift predictions and compared our findings. Since information on head orientation during craniotomy is not included in this database, we tested various plausible angles of head rotation. We analyzed the effects of brain tissue viscoelastic properties, mesh size, craniotomy position, CSF drainage level, and rigidity of meninges and then quantitatively evaluated the trade-off between accuracy and central processing unit time in predicting inward brain-shift across all models with second-order tetrahedral FEMs. The mean initial target registration error (TRE) was 5.78 ± 3.78 mm with rigid registration. FEM prediction (edge-length, 5 mm) with non-rigid meninges led to a mean TRE correction of 1.84 ± 0.83 mm assuming heterogeneous material. Results show that, for the low-grade glioma patients in the study, including non-rigid modeling of the meninges was significant statistically. In contrast including heterogeneity was not significant. To estimate the optimal head orientation and CSF drainage, an angle step of 5° and an CSF height step of 5 mm were enough leading to <0.26 mm TRE fluctuation.

A fast technique for hyper-echoic region separation from brain ultrasound images using patch based thresholding and cubic B-spline based contour smoothing

Ultrasound image guided brain surgery (UGBS) requires an automatic and fast image segmentation method. The level-set and active contour based algorithms have been found to be useful for obtaining topology-independent boundaries between different image regions. But slow convergence limits their use in online US image segmentation. The performance of these algorithms deteriorates on US images because of the intensity inhomogeneity. This paper proposes an effective region-driven method for the segmentation of hyper-echoic (HE) regions suppressing the hypo-echoic and anechoic regions in brain US images. An automatic threshold estimation scheme is developed with a modified Niblack's approach. The separation of the hyper-echoic and non-hyper-echoic (NHE) regions is performed by successively applying patch based intensity thresholding and boundary smoothing. First, a patch based segmentation is performed, which separates roughly the two regions. The patch based approach in this process reduces the effect of intensity heterogeneity within an HE region. An iterative boundary correction step with reducing patch size improves further the regional topology and refines the boundary regions. For avoiding the slope and curvature discontinuities and obtaining distinct boundaries between HE and NHE regions, a cubic B-spline model of curve smoothing is applied. The proposed method is 50-100 times faster than the other level-set based image segmentation algorithms. The segmentation performance and the convergence speed of the proposed method are compared with four other competing level-set based algorithms. The computational results show that the proposed segmentation approach outperforms other level-set based techniques both subjectively and objectively.

Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net

To compensate for the intraoperative brain tissue deformation, computer-assisted intervention methods have been used to register preoperative magnetic resonance images with intraoperative images. In order to model the deformation due to tissue resection, the resection cavity needs to be segmented in intraoperative images. We present an automatic method to segment the resection cavity in intraoperative ultrasound (iUS) images. We trained and evaluated two-dimensional (2-D) and three-dimensional (3-D) U-Net networks on two datasets of 37 and 13 cases that contain images acquired from different ultrasound systems. The best overall performing method was the 3-D network, which resulted in a 0.72 mean and 0.88 median Dice score over the whole dataset. The 2-D network also had good results with less computation time, with a median Dice score over 0.8. We also evaluated the sensitivity of network performance to training and testing with images from different ultrasound systems and image field of view. In this application, we found specialized networks to be more accurate for processing similar images than a general network trained with all the data. Overall, promising results were obtained for both datasets using specialized networks. This motivates further studies with additional clinical data, to enable training and validation of a clinically viable deep-learning model for automated delineation of the tumor resection cavity in iUS images.

Model-Based Image Updating for Brain Shift in Deep Brain Stimulation Electrode Placement Surgery

OBJECTIVE

The efficacy of deep brain stimulation (DBS) depends on accurate placement of electrodes. Although stereotactic frames enable co-registration of image-based surgical planning and the operative field, the accuracy of electrode placement can be degraded by intra-operative brain shift. In this study, we adapted a biomechanical model to estimate whole brain displacements from which we deformed preoperative CT (preCT) to generate an updated CT (uCT) that compensates for brain shift.

METHODS

We drove the deformation model using displacement data derived from deformation in the frontal cortical surface that occurred during the DBS intervention. We evaluated 15 patients, retrospectively, who underwent bilateral DBS surgery, and assessed the accuracy of uCT in terms of target registration error (TRE) relative to a CT acquired post-placement (postCT). We further divided subjects into large (Group L) and small (Group S) deformation groups based on a TRE threshold of 1.6mm. Anterior commissure (AC), posterior commissure (PC) and pineal gland (PG) were identified on preCT and postCT and used to quantify TREs in preCT and uCT.

RESULTS

In the group of large brain deformation, average TREs for uCT were 1.11 ± 0.13 and 1.07 ± 0.38 mm at AC and PC, respectively, compared to 1.85 ± 0.17 and 0.92 ± 0.52 mm for preCT. The model updating approach improved AC localization but did not alter TREs at PC.

CONCLUSION

This preliminary study suggests that our image updating method may compensate for brain shift around surgical targets of importance during DBS surgery, although further investigation is warranted before conclusive evidence will be available.

SIGNIFICANCE

With further development and evaluation, our model-based image updating method using intraoperative sparse data may compensate for brain shift in DBS surgery efficiently, and have utility in updating targeting coordinates.