Neurosurgery Core

Alexandra Golby Lauren O'Donnell Nathalie Agar
Alexandra Golby, MD
Core Lead
Lauren O'Donnell, PhD
Project Lead
Nathalie Agar, PhD
Project Lead

The neurosurgery project is developing new technologies toward the long-term goal of allowing neurosurgeons in diverse settings to implement the advantages of image-guided therapy (IGT) for their patients. We investigate, develop, and validate approaches that address the two key problems in brain tumor surgery: to define the critical brain regions that must not be resected, and to define the extent and nature of the lesion. Put more simply, we create tools that support the neurosurgeon’s crucial decision of what to preserve, and what to remove. Maximizing tumor resection improves patients’ progression-free survival and overall survival; avoiding neurological deficits also improves survival and deeply impacts daily life for patients. Our strategies leverage preoperative and intraoperative imaging data to optimize brain tumor surgery. We are focusing on multimodality imaging data including diffusion MRI (dMRI), functional MRI (fMRI), and on applying mass spectrometry (MS) as a molecular analysis tool for tumor detection. To improve understanding of critical, individual patient brain functional anatomy, we jointly model functional and structural data for semi-automatic and improved localization of eloquent brain structures. To guide surgical decision making by better defining tumor margins, we investigate MS as an intraoperative molecular diagnostic method. Achievement of these goals supports the overall goal of NCIGT that is relevant for brain tumor surgery: to maximize the extent of tumor resection while minimizing the risk of neurologic deficit. Our projects are:

Computer-aided individualized labeling of critical brain structures. fMRI and dMRI provide pre-operative non-invasive maps of patients’ functional activations and white matter connections. fMRI and dMRI have been shown to increase resection and time of survival, but their translation to widespread clinical use faces significant challenges. Interpretation of the data is difficult, requiring extensive experience and time, and requiring software tools that are unwieldy and not clinically oriented. In order to provide more useful pre-operative mapping, we create a system that produces labeled maps of individual brain functional anatomy, even in cases with missing data, distortion, edema, or reorganization. Our overall strategy is to model the anatomical relationship between structural connections and functional activations, and to build models designed to generalize to patients with mass lesions or displacement, with the aid of machine learning algorithms. We are investigating the following novel and complementary tools: labeling of fMRI activations to produce a segmentation of a discrete set of cortical features of importance for neurosurgery, semi-automatic fMRI thresholding, multimodal calculation of language lateralization, and iterative joint labeling of fMRI activations and fiber tracts. We are developing the computational tools in stages so that each tool can be used either alone, or as part of the full system. We especially focus on the challenge of language mapping interpretation that requires identification of both the crucial language-specific functional cortical regions and the crucial language-specific fiber tracts. We are validating results using expert raters and intraoperative electrocortical stimulation data. Overall, we are creating the first image analysis software that can semi-automatically produce a multimodal structure-function map of individual patient anatomy for neurosurgery. (Contact: Lauren O'Donnell)

Optimal resection guided by mass spectrometry. Intraoperative decision making regarding how much tissue to resect during brain tumor surgery is of critical importance, yet as the surgery progresses the surgeon has access to less and less reliable data to guide this decision. To optimize the surgical resection of brain tumors, surgeons need more information to assess the boundaries between tumor and healthy tissue. In order to give surgeons a better understanding of the tissue being resected, we are investigating MS as an intra-operative molecular analysis tool for surgical guidance in the Advanced Multimodality Image Guided Operating Suite (AMIGO). The introduction of MS into routine surgical protocols for real-time characterization of tissue relies on the development and validation of the molecular reference system. The current iteration of the intraoperative platform is based on an ambient ionization methodology that allows for the analysis of tissue with little to no sample preparation. We validate the technology for real-time identification of surgical margins and molecular diagnosis by comparing against standard histopathology. The neurosurgeon stereotactically samples multiple specimens from each brain tumor resection and these are analyzed with a mass spectrometer in the AMIGO suite. We also correlate molecular, imaging and histopathologic findings in the 3D tumor space. Overall, our goal is to provide data equivalent or better to intraoperative MRI with less workflow disruption, less cost, and far less infrastructure needs. (Contact: Nathalie Agar)

Software and Documentation

3D Slicer, a comprehensive open source platform for medical image analysis, contains several modules that have been contributed by us for Image-Guided Brain Tumor Surgery. These include:
  • UKF Tractography Two-tensor modeling with Kalman filtering to track through regions of crossing and edema.

  • White Matter Analysis Software for modeling and segmentation of white matter tracts. The output is visualized in 3D Slicer.

  • Diffusion MRI in 3D Slicer Diffusion magnetic resonance imaging in 3D Slicer open-source software.

Data

Presentations

These presentations have been selected as tutorials for readers interested in learning about the clinical science and technology of the Neurosurgery Core.

Links

Full Publication List in PubMed

Select Recent Publications

Zhang F, Breger A, Cho KIK, Ning L, Westin C-F, O'Donnell LJ, Pasternak O. Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI. Neuroimage. 2021;233 :117934.Abstract
Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.
Noh T, Mustroph M, Golby AJ. Intraoperative Imaging for High-Grade Glioma Surgery. Neurosurg Clin N Am. 2021;32 (1) :47-54.Abstract
This article discusses intraoperative imaging techniques used during high-grade glioma surgery. Gliomas can be difficult to differentiate from surrounding tissue during surgery. Intraoperative imaging helps to alleviate problems encountered during glioma surgery, such as brain shift and residual tumor. There are a variety of modalities available all of which aim to give the surgeon more information, address brain shift, identify residual tumor, and increase the extent of surgical resection. The article starts with a brief introduction followed by a review of with the latest advances in intraoperative ultrasound, intraoperative MRI, and intraoperative computed tomography.
Catalino MP, Yao S, Green DL, Laws ER, Golby AJ, Tie Y. Mapping Cognitive and Emotional Networks in Neurosurgical Patients Using Resting-State Functional Magnetic Resonance Imaging. Neurosurg Focus. 2020;48 (2) :E9.Abstract
Neurosurgery has been at the forefront of a paradigm shift from a localizationist perspective to a network-based approach to brain mapping. Over the last 2 decades, we have seen dramatic improvements in the way we can image the human brain and noninvasively estimate the location of critical functional networks. In certain patients with brain tumors and epilepsy, intraoperative electrical stimulation has revealed direct links between these networks and their function. The focus of these techniques has rightfully been identification and preservation of so-called "eloquent" brain functions (i.e., motor and language), but there is building momentum for more extensive mapping of cognitive and emotional networks. In addition, there is growing interest in mapping these functions in patients with a broad range of neurosurgical diseases. Resting-state functional MRI (rs-fMRI) is a noninvasive imaging modality that is able to measure spontaneous low-frequency blood oxygen level-dependent signal fluctuations at rest to infer neuronal activity. Rs-fMRI may be able to map cognitive and emotional networks for individual patients. In this review, the authors give an overview of the rs-fMRI technique and associated cognitive and emotional resting-state networks, discuss the potential applications of rs-fMRI, and propose future directions for the mapping of cognition and emotion in neurosurgical patients.
Stopa BM, Senders JT, Broekman MLD, Vangel M, Golby AJ. Preoperative Functional MRI Use in Neurooncology Patients: A Clinician Survey. Neurosurg Focus. 2020;48 (2) :E11.Abstract
OBJECTIVE: Functional MRI (fMRI) is increasingly being investigated for use in neurosurgical patient care. In the current study, the authors characterize the clinical use of fMRI by surveying neurosurgeons' use of and attitudes toward fMRI as a surgical planning tool in neurooncology patients. METHODS: A survey was developed to inquire about clinicians' use of and experiences with preoperative fMRI in the neurooncology patient population, including example case images. The survey was distributed to all neurosurgical departments with a residency program in the US. RESULTS: After excluding incomplete surveys and responders that do not use fMRI (n = 11), 50 complete responses were included in the final analysis. Responders were predominantly from academic programs (88%), with 20 years or more in practice (40%), with a main area of practice in neurooncology (48%) and treating an adult population (90%). All 50 responders currently use fMRI in neurooncology patients, mostly for low- (94%) and high-grade glioma (82%). The leading decision factors for ordering fMRI were location of mass in dominant hemisphere, location in a functional area, motor symptoms, and aphasia. Across 10 cases, language fMRI yielded the highest interrater reliability agreement (Fleiss' kappa 0.437). The most common reasons for ordering fMRI were to identify language laterality, plan extent of resection, and discuss neurological risks with patients. Clinicians reported that fMRI results were not obtained when ordered a median 10% of the time and were suboptimal a median 27% of the time. Of responders, 70% reported that they had ever resected an fMRI-positive functional site, of whom 77% did so because the site was "cleared" by cortical stimulation. Responders reported disagreement between fMRI and awake surgery 30% of the time. Overall, 98% of responders reported that if results of fMRI and intraoperative mapping disagreed, they would rely on intraoperative mapping. CONCLUSIONS: Although fMRI is increasingly being adopted as a practical preoperative planning tool for brain tumor resection, there remains a substantial degree of discrepancy with regard to its current use and presumed utility. There is a need for further research to evaluate the use of preoperative fMRI in neurooncology patients. As fMRI continues to gain prominence, it will be important for clinicians to collectively share best practices and develop guidelines for the use of fMRI in the preoperative planning phase of brain tumor patients.
Canalini L, Klein J, Miller D, Kikinis R. Enhanced Registration of Ultrasound Volumes by Segmentation of Resection Cavity in Neurosurgical Procedures. Int J Comput Assist Radiol Surg. 2020;15 (12) :1963-74.Abstract
PURPOSE: Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process. METHODS: The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity. RESULTS: Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm. CONCLUSIONS: We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery.
Lee TC, Guenette JP, Moses ZB, Lee JW, Annino DJ, Chi JH. MRI and CT Guided Cryoablation for Intracranial Extension of Malignancies along the Trigeminal Nerve. J Neurol Surg B Skull Base. 2020;81 (5) :511-4.Abstract
 To describe the technical aspects and early clinical outcomes of patients undergoing percutaneous magnetic resonance imaging (MRI)-guided tumor cryoablation along the intracranial trigeminal nerve. This study is a retrospective case review. Large academic tertiary care hospital. Patients who underwent MRI-guided cryoablation of perineural tumor along the intracranial trigeminal nerve. Technical success, pain relief, local control. Percutaneous MRI-guided cryoablation of tumor spread along the intracranial portion of the trigeminal nerve was performed in two patients without complication, with subsequent pain relief, and with local control in the patient with follow-up imaging. Percutaneous MRI-guided cryoablation is a feasible treatment option for malignancies tracking intracranially along the trigeminal nerve.
Narasimhan S, Weis JA, Luo M, Simpson AL, Thompson RC, Miga MI. Accounting for Intraoperative Brain Shift Ascribable to Cavity Collapse During Intracranial Tumor Resection. J Med Imaging (Bellingham). 2020;7 (3) :031506.Abstract
For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.
Rushmore RJ, Wilson-Braun P, Papadimitriou G, Ng I, Rathi Y, Zhang F, O'Donnell LJ, Kubicki M, Bouix S, Yeterian E, et al. 3D Exploration of the Brainstem in 50-Micron Resolution MRI. Front Neuroanat. 2020;14 :40.Abstract
The brainstem, a structure of vital importance in mammals, is currently becoming a principal focus in cognitive, affective, and clinical neuroscience. Midbrain, pontine and medullary structures serve as the conduit for signals between the forebrain and spinal cord, are the epicenter of cranial nerve-circuits and systems, and subserve such integrative functions as consciousness, emotional processing, pain, and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high-resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases. Given the unique capability of MRI-based morphometric analysis in generating and preserving the morphology of 3D objects from individual 2D sections, we reconstructed the motor, sensory and integrative neural systems of the brainstem and rendered them in 3D representations. We anticipate the utilization of these maps by the neuroimaging community for applications in basic neuroscience as well as in neurology, psychiatry, and neurosurgery, due to their versatile computational nature in 2D and 3D representations in a publicly available capacity.
Zhang F, Xie G, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, et al. Creation of a Novel Trigeminal Tractography Atlas for Automated Trigeminal Nerve Identification. Neuroimage. 2020;220 :117063.Abstract
Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.
Epprecht L, Qureshi A, Kozin ED, Vachicouras N, Huber AM, Kikinis R, Makris N, Brown CM, Reinshagen KL, Lee DJ. Human Cochlear Nucleus on 7 Tesla Diffusion Tensor Imaging: Insights Into Micro-anatomy and Function for Auditory Brainstem Implant Surgery. Otol Neurotol. 2020;41 (4) :e484-e493.Abstract
OBJECTIVE: The cochlear nucleus (CN) is the target of the auditory brainstem implant (ABI). Most ABI candidates have Neurofibromatosis Type 2 (NF2) and distorted brainstem anatomy from bilateral vestibular schwannomas. The CN is difficult to characterize as routine structural MRI does not resolve detailed anatomy. We hypothesize that diffusion tensor imaging (DTI) enables both in vivo localization and quantitative measurements of CN morphology. STUDY DESIGN: We analyzed 7 Tesla (T) DTI images of 100 subjects (200 CN) and relevant anatomic structures using an MRI brainstem atlas with submillimetric (50 μm) resolution. SETTING: Tertiary referral center. PATIENTS: Young healthy normal hearing adults. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Diffusion scalar measures such as fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (Mode), principal eigenvectors of the CN, and the adjacent inferior cerebellar peduncle (ICP). RESULTS: The CN had a lamellar structure and ventral-dorsal fiber orientation and could be localized lateral to the inferior cerebellar peduncle (ICP). This fiber orientation was orthogonal to tracts of the adjacent ICP where the fibers run mainly caudal-rostrally. The CN had lower FA compared to the medial aspect of the ICP (0.44 ± 0.09 vs. 0.64 ± 0.08, p < 0.001). CONCLUSIONS: 7T DTI enables characterization of human CN morphology and neuronal substructure. An ABI array insertion vector directed more caudally would better correspond to the main fiber axis of CN. State-of-the-art DTI has implications for ABI preoperative planning and future image guidance-assisted placement of the electrode array.
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