Visualization of Brain Shift Corrected Functional Magnetic Resonance Imaging Data for Intraoperative Brain Mapping

Sensorimotor Physiological Mapping During Asleep Deep Brain Stimulation Lead Placement With Multichannel Intraoperative Neuromonitoring

BACKGROUND AND OBJECTIVES

Lead placement during asleep deep brain stimulation (DBS) surgery has relied primarily on intraoperative imaging, forgoing traditional awake neurophysiological testing. We aimed to describe our experience of asleep macrostimulation mapping of implanted DBS leads using intraoperative neuromonitoring (IONM) techniques, which were used to guide electrode placement-in addition to intraoperative computed tomography imaging and in place of awake neurophysiological testing.

METHODS

This was a single institution retrospective study of asleep DBS surgery with IONM mapping for Parkinson's disease, ET, and dystonia targeting the ventral intermediate nucleus, subthalamic nucleus, and globus pallidus interna.

RESULTS

A series of 88 consecutive patients from a single surgeon were included. 67 patients received DBS for Parkinson's disease, 14 for essential tremor, and 7 for dystonia. The DBS target was globus pallidus interna for 60 patients, subthalamic nucleus for 14, and ventral intermediate nucleus for 14, with 95.5% undergoing bilateral lead placement. The mean single stage surgery time was 170 minutes. No patients required surgical lead revision, and no unanticipated sensorimotor side effects were noted during DBS programming. Compared with patients undergoing awake DBS surgery, there was no significant difference in patient-reported outcomes.

CONCLUSION

Asleep IONM mapping offers valuable physiological data to guide electrode asleep DBS placement and complement intraoperative imaging techniques.

An update on tests used for intraoperative monitoring of cognition during awake craniotomy

PURPOSE

Mapping higher-order cognitive functions during awake brain surgery is important for cognitive preservation which is related to postoperative quality of life. A systematic review from 2018 about neuropsychological tests used during awake craniotomy made clear that until 2017 language was most often monitored and that the other cognitive domains were underexposed (Ruis, J Clin Exp Neuropsychol 40(10):1081-1104, 218). The field of awake craniotomy and cognitive monitoring is however developing rapidly. The aim of the current review is therefore, to investigate whether there is a change in the field towards incorporation of new tests and more complete mapping of (higher-order) cognitive functions.

METHODS

We replicated the systematic search of the study from 2018 in PubMed and Embase from February 2017 to November 2023, yielding 5130 potentially relevant articles. We used the artificial machine learning tool ASReview for screening and included 272 papers that gave a detailed description of the neuropsychological tests used during awake craniotomy.

RESULTS

Comparable to the previous study of 2018, the majority of studies (90.4%) reported tests for assessing language functions (Ruis, J Clin Exp Neuropsychol 40(10):1081-1104, 218). Nevertheless, an increasing number of studies now also describe tests for monitoring visuospatial functions, social cognition, and executive functions.

CONCLUSIONS

Language remains the most extensively tested cognitive domain. However, a broader range of tests are now implemented during awake craniotomy and there are (new developed) tests which received more attention. The rapid development in the field is reflected in the included studies in this review. Nevertheless, for some cognitive domains (e.g., executive functions and memory), there is still a need for developing tests that can be used during awake surgery.

Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection

PURPOSE

In brain tumor surgery, tissue shift (called brain shift) can move the surgical target and invalidate the surgical plan. A cost-effective and flexible tool, intra-operative ultrasound (iUS) with robust image registration algorithms can effectively track brain shift to ensure surgical outcomes and safety.

METHODS

We proposed to employ a Siamese neural network, which was first trained using natural images and fine-tuned with domain-specific data to automatically detect matching anatomical landmarks in iUS scans at different surgical stages. An efficient 2.5D approach and an iterative re-weighted least squares algorithm are utilized to perform landmark-based registration for brain shift correction. The proposed method is validated and compared against the state-of-the-art methods using the public BITE and RESECT datasets.

RESULTS

Registration of pre-resection iUS scans to during- and post-resection iUS images were executed. The results with the proposed method shows a significant improvement from the initial misalignment ([Formula: see text]) and the method is comparable to the state-of-the-art methods validated on the same datasets.

CONCLUSIONS

We have proposed a robust technique to efficiently detect matching landmarks in iUS and perform brain shift correction with excellent performance. It has the potential to improve the accuracy and safety of neurosurgery.

Intraoperative Brain Mapping in Multilingual Patients: What Do We Know and Where Are We Going?

In this review, we evaluate the knowledge gained so far about the neural bases of multilingual language processing obtained mainly through imaging and electrical stimulation mapping (ESM). We attempt to answer some key questions about multilingualism in the light of recent literature evidence, such as the degree of anatomical–functional integration of two or more languages in a multilingual brain, how the age of L2-acquisition affects language organization in the human brain, or how the brain controls more than one language. Finally, we highlight the future trends in multilingual language mapping.

Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Purpose

Computed tomography perfusion (CTP) is used to diagnose ischemic strokes through contralateral hemisphere comparisons of various perfusion parameters. Various perfusion parameter thresholds have been utilized to segment infarct tissue due to differences in CTP software and patient baseline hemodynamics. This study utilized a convolutional neural network (CNN) to eliminate the need for non-universal parameter thresholds to segment infarct tissue.

Methods

CTP data from 63 ischemic stroke patients was retrospectively collected and perfusion parameter maps were generated using Vitrea CTP software. Infarct ground truth labels were segmented from diffusion-weighted imaging (DWI) and CTP and DWI volumes were registered. A U-net based CNN was trained and tested five separate times using each CTP parameter (cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak (TTP), mean-transit-time (MTT), delay time). 8,352 infarct slices were utilized with a 60:30:10 training:testing:validation split and Monte Carlo cross-validation was conducted using 20 iterations. Infarct volumes were reconstructed following segmentation from each CTP slice. Infarct spatial and volumetric agreement was compared between each CTP parameter and DWI.

Results

Spatial agreement metrics (Dice coefficient, positive predictive value) for each CTP parameter in predicting infarct volumes are: CBF=(0.67, 0.76), CBV=(0.44, 0.62), TTP=(0.60, 0.67), MTT=(0.58, 0.62), delay time=(0.57, 0.60). 95% confidence intervals for volume differences with DWI infarct are: CBF=14.3±11.5 mL, CBV=29.6±21.2 mL, TTP=7.7±15.2 mL, MTT=-10.7±18.6 mL, delay time=-5.7±23.6 mL.

Conclusions

CBF is the most accurate CTP parameter in segmenting infarct tissue. Segmentation of infarct using a CNN has the potential to eliminate non-universal CTP contralateral hemisphere comparison thresholds.

Assessment of computed tomography perfusion software in predicting spatial location and volume of infarct in acute ischemic stroke patients: a comparison of Sphere, Vitrea, and RAPID

BACKGROUND

CT perfusion (CTP) infarct and penumbra estimations determine the eligibility of patients with acute ischemic stroke (AIS) for endovascular intervention. This study aimed to determine volumetric and spatial agreement of predicted RAPID, Vitrea, and Sphere CTP infarct with follow-up fluid attenuation inversion recovery (FLAIR) MRI infarct.

METHODS

108 consecutive patients with AIS and large vessel occlusion were included in the study between April 2019 and January 2020 . Patients were divided into two groups: endovascular intervention (n=58) and conservative treatment (n=50). Intervention patients were treated with mechanical thrombectomy and achieved successful reperfusion (Thrombolysis in Cerebral Infarction 2b/2 c/3) while patients in the conservative treatment group did not receive mechanical thrombectomy or intravenous thrombolysis. Intervention and conservative treatment patients were included to assess infarct and penumbra estimations, respectively. It was assumed that in all patients treated conservatively, penumbra converted to infarct. CTP infarct and penumbra volumes were segmented from RAPID, Vitrea, and Sphere to assess volumetric and spatial agreement with follow-up FLAIR MRI.

RESULTS

Mean infarct differences (95% CIs) between each CTP software and FLAIR MRI for each cohort were: intervention cohort: RAPID=9.0±7.7 mL, Sphere=-0.2±8.7 mL, Vitrea=-7.9±8.9 mL; conservative treatment cohort: RAPID=-31.9±21.6 mL, Sphere=-26.8±17.4 mL, Vitrea=-15.3±13.7 mL. Overlap and Dice coefficients for predicted infarct were (overlap, Dice): intervention cohort: RAPID=(0.57, 0.44), Sphere=(0.68, 0.60), Vitrea=(0.70, 0.60); conservative treatment cohort: RAPID=(0.71, 0.56), Sphere=(0.73, 0.60), Vitrea=(0.72, 0.64).

CONCLUSIONS

Sphere proved the most accurate in patients who had intervention infarct assessment as Vitrea and RAPID overestimated and underestimated infarct, respectively. Vitrea proved the most accurate in penumbra assessment for patients treated conservatively although all software overestimated penumbra.

Investigation of convolutional neural networks using multiple computed tomography perfusion maps to identify infarct core in acute ischemic stroke patients

Purpose: To assess acute ischemic stroke (AIS) severity, infarct is segmented using computed tomography perfusion (CTP) software, such as RAPID, Sphere, and Vitrea, relying on contralateral hemisphere thresholds. Since this approach is potentially patient dependent, we investigated whether convolutional neural networks (CNNs) could achieve better performances without the need for contralateral hemisphere thresholds. Approach: CTP and diffusion-weighted imaging (DWI) data were retrospectively collected for 63 AIS patients. Cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak, mean-transit-time (MTT), and delay time maps were generated using Vitrea CTP software. U-net shaped CNNs were developed, trained, and tested for 26 different input CTP parameter combinations. Infarct labels were segmented from DWI volumes registered with CTP volumes. Infarct volumes were reconstructed from two-dimensional CTP infarct segmentations. To remove erroneous segmentations, conditional random field (CRF) postprocessing was applied and compared with prior results. Spatial and volumetric infarct agreement was assessed between DWI and CTP (CNNs and commercial software) using median infarct difference, median absolute error, dice coefficient, positive predictive value. Results: The most accurate combination of parameters for CNN segmenting infarct using CRF postprocessing was CBF, CBV, and MTT (4.83 mL, 10.14 mL, 0.66, 0.73). Commercial software results are: RAPID = (2.25 mL, 21.48 mL, 0.63, 0.70), Sphere = (7.57 mL, 17.74 mL, 0.64, 0.70), Vitrea = (6.79 mL, 15.28 mL, 0.63, 0.72). Conclusions: Use of CNNs with multiple input perfusion parameters has shown to be accurate in segmenting infarcts and has the ability to improve clinical workflow by eliminating the need for contralateral hemisphere comparisons.

Performance of angiographic parametric imaging in locating infarct core in large vessel occlusion acute ischemic stroke patients

Purpose: Biomarkers related to hemodynamics can be quantified using angiographic parametric imaging (API), which is a quantitative imaging method that uses digital subtraction angiography (DSA). We aimed to assess the accuracy of API in locating infarct core within large vessel occlusion (LVO) acute ischemic stroke (AIS) patients. Approach: Data were retrospectively collected for 25 LVO AIS patients who achieved successful recanalization. DSA data from lateral and anteroposterior (AP) views were loaded into API software to generate hemodynamic parameter maps. Relative differences in hemispherical regions for each API parameter were calculated. Ground truth infarct core locations were obtained using 24-h follow-up fluid-attenuation inversion recovery (FLAIR) MRI imaging. FLAIR MRI infarct locations were registered with DSA images to determine infarct regions in API parameter maps. Relative differences across hemispheres for each API parameter were plotted against each other. A support vector machine was used to determine the optimal hyperplane for classifying regions as infarct or healthy tissue. Results: For the lateral and AP views, respectively, the most accurate classification of infarct regions came from plotting mean transit time (MTT) versus peak height (PH) [ accuracy = 0.8125 ± 0.0012 (95%)], the area under the receiver operator characteristic curve ( AUROC ) = 0.8946 ± 0.0000 (95%), and plotting MTT versus the area under the curve (AUC) [ accuracy = 0.7957 ± 0.0011 (95%), AUROC = 0.8759 ± 0.0000 (95%)]. Conclusions: API provides accurate assessment of locating ischemic core in AIS LVO patients and has the potential for clinical benefit by determining infarct core location and growth in real time for intraoperative decision making.