Neurosurgery Research Publications

2020
Thomas C Lee, Jeffrey P Guenette, Ziev B Moses, Jong Woo Lee, Donald J Annino, and John H Chi. 10/2020. “MRI and CT Guided Cryoablation for Intracranial Extension of Malignancies along the Trigeminal Nerve.” J Neurol Surg B Skull Base, 81, 5, Pp. 511-514.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.
Richard Jarrett Rushmore, Peter Wilson-Braun, George Papadimitriou, Isaac Ng, Yogesh Rathi, Fan Zhang, Lauren Jean O'Donnell, Marek Kubicki, Sylvain Bouix, Edward Yeterian, Jean-Jacques Lemaire, Evan Calabrese, Allan G Johnson, Ron Kikinis, and Nikos Makris. 9/2020. “3D Exploration of the Brainstem in 50-Micron Resolution MRI.” Front Neuroanat, 14, Pp. 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.
Nityanand Miskin, Prashin Unadkat, Michael E Carlton, Alexandra J Golby, Geoffrey S Young, and Raymond Y Huang. 8/2020. “Frequency and Evolution of New Postoperative Enhancement on 3 Tesla Intraoperative and Early Postoperative Magnetic Resonance Imaging.” Neurosurgery, 87, 2, Pp. 238-46.Abstract
BACKGROUND: Intraoperative magnetic resonance imaging (IO-MRI) provides real-time assessment of extent of resection of brain tumor. Development of new enhancement during IO-MRI can confound interpretation of residual enhancing tumor, although the incidence of this finding is unknown. OBJECTIVE: To determine the frequency of new enhancement during brain tumor resection on intraoperative 3 Tesla (3T) MRI. To optimize the postoperative imaging window after brain tumor resection using 1.5 and 3T MRI. METHODS: We retrospectively evaluated 64 IO-MRI performed for patients with enhancing brain lesions referred for biopsy or resection as well as a subset with an early postoperative MRI (EP-MRI) within 72 h of surgery (N = 42), and a subset with a late postoperative MRI (LP-MRI) performed between 120 h and 8 wk postsurgery (N = 34). Three radiologists assessed for new enhancement on IO-MRI, and change in enhancement on available EP-MRI and LP-MRI. Consensus was determined by majority response. Inter-rater agreement was assessed using percentage agreement. RESULTS: A total of 10 out of 64 (16%) of the IO-MRI demonstrated new enhancement. Seven of 10 patients with available EP-MRI demonstrated decreased/resolved enhancement. One out of 42 (2%) of the EP-MRI demonstrated new enhancement, which decreased on LP-MRI. Agreement was 74% for the assessment of new enhancement on IO-MRI and 81% for the assessment of new enhancement on the EP-MRI. CONCLUSION: New enhancement occurs in intraoperative 3T MRI in 16% of patients after brain tumor resection, which decreases or resolves on subsequent MRI within 72 h of surgery. Our findings indicate the opportunity for further study to optimize the postoperative imaging window.
Adomas Bunevicius, Nathan Judson McDannold, and Alexandra J Golby. 7/2020. “Focused Ultrasound Strategies for Brain Tumor Therapy.” Oper Neurosurg (Hagerstown), 19, 1, Pp. 9-18.Abstract
BACKGROUND: A key challenge in the medical treatment of brain tumors is the limited penetration of most chemotherapeutic agents across the blood-brain barrier (BBB) into the tumor and the infiltrative margin around the tumor. Magnetic resonance-guided focused ultrasound (MRgFUS) is a promising tool to enhance the delivery of chemotherapeutic agents into brain tumors. OBJECTIVE: To review the mechanism of FUS, preclinical evidence, and clinical studies that used low-frequency FUS for a BBB opening in gliomas. METHODS: Literature review. RESULTS: The potential of externally delivered low-intensity ultrasound for a temporally and spatially precise and predictable disruption of the BBB has been investigated for over a decade, yielding extensive preclinical literature demonstrating that FUS can disrupt the BBB in a spatially targeted and temporally reversible manner. Studies in animal models documented that FUS enhanced the delivery of numerous chemotherapeutic and investigational agents across the BBB and into brain tumors, including temozolomide, bevacizumab, 1,3-bis (2-chloroethyl)-1-nitrosourea, doxorubicin, viral vectors, and cells. Chemotherapeutic interventions combined with FUS slowed tumor progression and improved animal survival. Recent advances of MRgFUS systems allow precise, temporally and spatially controllable, and safe transcranial delivery of ultrasound energy. Initial clinical evidence in glioma patients has shown the efficacy of MRgFUS in disrupting the BBB, as demonstrated by an enhanced gadolinium penetration. CONCLUSION: Thus far, a temporary disruption of the BBB followed by the administration of chemotherapy has been both feasible and safe. Further studies are needed to determine the actual drug delivery, including the drug distribution at a tissue-level scale, as well as effects on tumor growth and patient prognosis.
Fan Zhang, Guoqiang Xie, Laura Leung, Michael A Mooney, Lorenz Epprecht, Isaiah Norton, Yogesh Rathi, Ron Kikinis, Ossama Al-Mefty, Nikos Makris, Alexandra J Golby, and Lauren J O'Donnell. 6/2020. “Creation of a Novel Trigeminal Tractography Atlas for Automated Trigeminal Nerve Identification.” Neuroimage, 220, Pp. 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.
Fan Zhang, Suheyla Cetin Karayumak, Nico Hoffmann, Yogesh Rathi, Alexandra J Golby, and Lauren J O'Donnell. 6/2020. “Deep White Matter Analysis (DeepWMA): Fast and Consistent Tractography Segmentation.” Med Image Anal, 65, Pp. 101761.Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
Saramati Narasimhan, Jared A Weis, Ma Luo, Amber L Simpson, Reid C Thompson, and Michael I Miga. 5/2020. “Accounting for Intraoperative Brain Shift Ascribable to Cavity Collapse During Intracranial Tumor Resection.” J Med Imaging (Bellingham), 7, 3, Pp. 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.
Shun Yao, Pan Lin, Matthew Vera, Farhana Akter, Ru-Yuan Zhang, Ailiang Zeng, Alexandra J Golby, Guozheng Xu, Yanmei Tie, and Jian Song. 4/2020. “Hormone Levels are Related to Functional Compensation in Prolactinomas: A Resting-state fMRI Study.” J Neurol Sci, 411, Pp. 116720.Abstract
Prolactinomas are tumors of the pituitary gland, which overproduces prolactin leading to dramatic fluctuations of endogenous hormone levels throughout the body. While it is not fully understood how endogenous hormone disorders affect a patient's brain, it is well known that fluctuating hormone levels can have negative neuropsychological effects. Using resting-state functional magnetic resonance imaging (rs-fMRI), we investigated whole-brain functional connectivity (FC) and its relationship with hormone levels in prolactinomas. By performing seed-based FC analyses, we compared FC metrics between 33 prolactinoma patients and 31 healthy controls matched for age, sex, and hand dominance. We then carried out a partial correlation analysis to examine the relationship between FC metrics and hormone levels. Compared to healthy controls, prolactinoma patients showed significantly increased thalamocortical and cerebellar-cerebral FC. Endogenous hormone levels were also positively correlated with increased FC metrics, and these hormone-FC relationships exhibited sex differences in prolactinoma patients. Our study is the first to reveal altered FC patterns in prolactinomas and to quantify the hormone-FC relationships. These results indicate the importance of endogenous hormones on functional compensation of the brain in patients with prolactinomas.
Lorenz Epprecht, Ahad Qureshi, Elliott D Kozin, Nicolas Vachicouras, Alexander M Huber, Ron Kikinis, Nikos Makris, Christian M Brown, Katherine L Reinshagen, and Daniel J Lee. 4/2020. “Human Cochlear Nucleus on 7 Tesla Diffusion Tensor Imaging: Insights Into Micro-anatomy and Function for Auditory Brainstem Implant Surgery.” Otol Neurotol, 41, 4, Pp. 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.
Rachel A Freedman, Rebecca S Gelman, Nathalie YR Agar, Sandro Santagata, Elizabeth C Randall, Begoña Gimenez-Cassina Lopez, Roisin M Connolly, Ian F Dunn, Catherine H Van Poznak, Carey K Anders, Michelle E Melisko, Kelly Silvestri, Christine M Cotter, Kathryn P Componeschi, Juan M Marte, Beverly Moy, Kimberly L Blackwell, Shannon L Puhalla, Nuhad Ibrahim, Timothy J Moynihan, Julie Nangia, Nadine Tung, Robyn Burns, Mothaffar F Rimawi, Ian E Krop, Antonio C Wolff, Eric P Winer, Nancy U Lin, and Nancy U Lin. 4/2020. “Pre- and Postoperative Neratinib for HER2-Positive Breast Cancer Brain Metastases: Translational Breast Cancer Research Consortium 022.” Clin Breast Cancer, 20, 2, Pp. 145-51.Abstract
PURPOSE: This pilot study was performed to test our ability to administer neratinib monotherapy before clinically recommended craniotomy in patients with HER2-positive metastatic breast cancer to the central nervous system, to examine neratinib's central nervous system penetration at craniotomy, and to examine postoperative neratinib maintenance. PATIENTS AND METHODS: Patients with HER2-positive brain metastases undergoing clinically indicated cranial resection of a parenchymal tumor received neratinib 240 mg orally once a day for 7 to 21 days preoperatively, and resumed therapy postoperatively in 28-day cycles. Exploratory evaluations of time to disease progression, survival, and correlative tissue, cerebrospinal fluid (CSF), and blood-based analyses examining neratinib concentrations were planned. The study was registered at ClinicalTrials.gov under number NCT01494662. RESULTS: We enrolled 5 patients between May 22, 2013, and October 18, 2016. As of March 1, 2019, patients had remained on the study protocol for 1 to 75+ postoperative cycles pf therapy. Two patients had grade 3 diarrhea. Evaluation of the CSF showed low concentrations of neratinib; nonetheless, 2 patients continued to receive therapy without disease progression for at least 13 cycles, with one on-study treatment lasting for nearly 6 years. Neratinib distribution in surgical tissue was variable for 1 patient, while specimens from 2 others did not produce conclusive results as a result of limited available samples. CONCLUSION: Neratinib resulted in expected rates of diarrhea in this small cohort, with 2 of 5 patients receiving the study treatment for durable periods. Although logistically challenging, we were able to test a limited number of CSF- and parenchymal-based neratinib concentrations. Our findings from resected tumor tissue in one patient revealed heterogeneity in drug distribution and tumor histopathology.
Laura Rigolo, Walid Ibn Essayed, Yanmei Tie, Isaiah Norton, Srinivasan Mukundan, and Alexandra Golby. 3/2020. “Intraoperative Use of Functional MRI for Surgical Decision Making after Limited or Infeasible Electrocortical Stimulation Mapping.” J Neuroimaging, 30, 2, Pp. 184-91.Abstract
BACKGROUND AND PURPOSE: Functional magnetic resonance imaging (fMRI) is becoming widely recognized as a key component of preoperative neurosurgical planning, although intraoperative electrocortical stimulation (ECS) is considered the gold standard surgical brain mapping method. However, acquiring and interpreting ECS results can sometimes be challenging. This retrospective study assesses whether intraoperative availability of fMRI impacted surgical decision-making when ECS was problematic or unobtainable. METHODS: Records were reviewed for 191 patients who underwent presurgical fMRI with fMRI loaded into the neuronavigation system. Four patients were excluded as a bur-hole biopsy was performed. Imaging was acquired at 3 Tesla and analyzed using the general linear model with significantly activated pixels determined via individually determined thresholds. fMRI maps were displayed intraoperatively via commercial neuronavigation systems. RESULTS: Seventy-one cases were planned ECS; however, 18 (25.35%) of these procedures were either not attempted or aborted/limited due to: seizure (10), patient difficulty cooperating with the ECS mapping (4), scarring/limited dural opening (3), or dural bleeding (1). In all aborted/limited ECS cases, the surgeon continued surgery using fMRI to guide surgical decision-making. There was no significant difference in the incidence of postoperative deficits between cases with completed ECS and those with limited/aborted ECS. CONCLUSIONS: Preoperative fMRI allowed for continuation of surgery in over one-fourth of patients in which planned ECS was incomplete or impossible, without a significantly different incidence of postoperative deficits compared to the patients with completed ECS. This demonstrates additional value of fMRI beyond presurgical planning, as fMRI data served as a backup method to ECS.
Elizabeth C Randall, Begoña GC Lopez, Sen Peng, Michael S Regan, Walid M Abdelmoula, Sankha S Basu, Sandro Santagata, Haejin Yoon, Marcia C Haigis, Jeffrey N Agar, Nhan L Tran, William F Elmquist, Forest M White, Jann N Sarkaria, and Nathalie YR Agar. 3/2020. “Localized Metabolomic Gradients in Patient-Derived Xenograft Models of Glioblastoma.” Cancer Res, 80, 6, Pp. 1258-67.Abstract
Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood-brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. SIGNIFICANCE: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment..
Guoqiang Xie, Fan Zhang, Laura Leung, Michael A Mooney, Lorenz Epprecht, Isaiah Norton, Yogesh Rathi, Ron Kikinis, Ossama Al-Mefty, Nikos Makris, Alexandra J Golby, and Lauren J O'Donnell. 1/2020. “Anatomical Assessment of Trigeminal Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition B-Values and Single- and Multi-Fiber Tracking Strategies.” Neuroimage Clin, 25, Pp. 102160.Abstract
BACKGROUND: The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN. OBJECTIVE: We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN. METHODS: We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm, b = 2000 s/mm and b = 3000 s/mm, using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result. RESULTS: The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result. CONCLUSIONS: Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI.
Adomas Bunevicius, Katharina Schregel, Ralph Sinkus, Alexandra Golby, and Samuel Patz. 1/2020. “REVIEW: MR Elastography of Brain Tumors.” Neuroimage Clin, 25, Pp. 102109.Abstract
MR elastography allows non-invasive quantification of the shear modulus of tissue, i.e. tissue stiffness and viscosity, information that offers the potential to guide presurgical planning for brain tumor resection. Here, we review brain tumor MRE studies with particular attention to clinical applications. Studies that investigated MRE in patients with intracranial tumors, both malignant and benign as well as primary and metastatic, were queried from the Pubmed/Medline database in August 2018. Reported tumor and normal appearing white matter stiffness values were extracted and compared as a function of tumor histopathological diagnosis and MRE vibration frequencies. Because different studies used different elastography hardware, pulse sequences, reconstruction inversion algorithms, and different symmetry assumptions about the mechanical properties of tissue, effort was directed to ensure that similar quantities were used when making inter-study comparisons. In addition, because different methodologies and processing pipelines will necessarily bias the results, when pooling data from different studies, whenever possible, tumor values were compared with the same subject's contralateral normal appearing white matter to minimize any study-dependent bias. The literature search yielded 10 studies with a total of 184 primary and metastatic brain tumor patients. The group mean tumor stiffness, as measured with MRE, correlated with intra-operatively assessed stiffness of meningiomas and pituitary adenomas. Pooled data analysis showed significant overlap between shear modulus values across brain tumor types. When adjusting for the same patient normal appearing white matter shear modulus values, meningiomas were the stiffest tumor-type. MRE is increasingly being examined for potential in brain tumor imaging and might have value for surgical planning. However, significant overlap of shear modulus values between a number of different tumor types limits applicability of MRE for diagnostic purposes. Thus, further rigorous studies are needed to determine specific clinical applications of MRE for surgical planning, disease monitoring and molecular stratification of brain tumors.
2019
Fan Zhang, Nico Hoffmann, Suheyla Cetin Karayumak, Yogesh Rathi, Alexandra J Golby, and Lauren J O'Donnell. 10/2019. “Deep White Matter Analysis: Fast, Consistent Tractography Segmentation Across Populations and dMRI Acquisitions.” Med Image Comput Comput Assist Interv, 11766, Pp. 599-608.Abstract
We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a CNN tract classification model based on FiberMap and obtain a high tract classification accuracy of 90.99%. The method is evaluated on a test dataset of 374 dMRI scans from three independently acquired populations across health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art white matter tract segmentation methods. Experimental results show that our method obtains a highly consistent segmentation result, where over 99% of the fiber tracts are successfully detected across all subjects under study, most importantly, including patients with space occupying brain tumors. The proposed method leverages deep learning techniques and provides a much faster and more efficient tool for large data analysis than methods using traditional machine learning techniques.
S Frisken, M Luo, I Machado, P Unadkat, P Juvekar, A Bunevicius, M Toews, WM Wells, MI Miga, and AJ Golby. 2/2019. “Preliminary Results Comparing Thin Plate Splines with Finite Element Methods for Modeling Brain Deformation during Neurosurgery using Intraoperative Ultrasound.” Proc SPIE Int Soc Opt Eng, 10951, Pp. 1095120.Abstract
Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.
J Nitsch, J Klein, P Dammann, K Wrede, O Gembruch, JH Moltz, H Meine, U Sure, R. Kikinis, and D Miller. 2019. “Automatic and Efficient MRI-US Segmentations for Improving Intraoperative Image Fusion in Image-guided Neurosurgery.” Neuroimage Clin, 22, Pp. 101766.Abstract
Knowledge of the exact tumor location and structures at risk in its vicinity are crucial for neurosurgical interventions. Neuronavigation systems support navigation within the patient's brain, based on preoperative MRI (preMRI). However, increasing tissue deformation during the course of tumor resection reduces navigation accuracy based on preMRI. Intraoperative ultrasound (iUS) is therefore used as real-time intraoperative imaging. Registration of preMRI and iUS remains a challenge due to different or varying contrasts in iUS and preMRI. Here, we present an automatic and efficient segmentation of B-mode US images to support the registration process. The falx cerebri and the tentorium cerebelli were identified as examples for central cerebral structures and their segmentations can serve as guiding frame for multi-modal image registration. Segmentations of the falx and tentorium were performed with an average Dice coefficient of 0.74 and an average Hausdorff distance of 12.2 mm. The subsequent registration incorporates these segmentations and increases accuracy, robustness and speed of the overall registration process compared to purely intensity-based registration. For validation an expert manually located corresponding landmarks. Our approach reduces the initial mean Target Registration Error from 16.9 mm to 3.8 mm using our intensity-based registration and to 2.2 mm with our combined segmentation and registration approach. The intensity-based registration reduced the maximum initial TRE from 19.4 mm to 5.6 mm, with the approach incorporating segmentations this is reduced to 3.0 mm. Mean volumetric intensity-based registration of preMRI and iUS took 40.5 s, including segmentations 12.0 s.
Inês Machado, Matthew Toews, Elizabeth George, Prashin Unadkat, Walid Essayed, Jie Luo, Pedro Teodoro, Herculano Carvalho, Jorge Martins, Polina Golland, Steve Pieper, Sarah Frisken, Alexandra Golby, William Wells, and Yangming Ou. 2019. “Deformable MRI-Ultrasound Registration using Correlation-based Attribute Matching for Brain Shift Correction: Accuracy and Generality in Multi-site Data.” Neuroimage, 202, Pp. 116094.Abstract
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (US) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy US. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. To improve accuracy of registration, we use high-dimensional texture attributes instead of image intensities and propose to replace the standard difference-based attribute matching with correlation-based attribute matching. We also present a strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images. We optimize key parameters across independent MR-iUS brain tumor datasets acquired at three different institutions, with a total of 43 tumor patients and 758 corresponding landmarks to validate the registration algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, our algorithm was able to reduce landmark errors prior to registration in three data sets (5.37 ± 4.27, 4.18 ± 1.97 and 6.18 ± 3.38 mm, respectively) to a consistently low level (2.28 ± 0.71, 2.08 ± 0.37 and 2.24 ± 0.78 mm, respectively). Our algorithm is compared to 15 other algorithms that have been previously tested on MR-iUS registration and it is competitive with the state-of-the-art on multiple datasets. We show that our algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). We further characterized landmark errors according to brain regions and tumor types, a topic so far missing in the literature. We found that landmark errors were higher in high-grade than low-grade glioma patients, and higher in tumor regions than in other brain regions.
Prashin Unadkat, Luca Fumagalli, Laura Rigolo, Mark G Vangel, Geoffrey S Young, Raymond Huang, Srinivasan Mukundan, Alexandra Golby, and Yanmei Tie. 2019. “Functional MRI Task Comparison for Language Mapping in Neurosurgical Patients.” J Neuroimaging, 29, 3, Pp. 348-56.Abstract
BACKGROUND AND PURPOSE: Language task-based functional MRI (fMRI) is increasingly used for presurgical planning in patients with brain lesions. Different paradigms elicit activations of different components of the language network. The aim of this study is to optimize fMRI clinical usage by comparing the effectiveness of three language tasks for language lateralization and localization in a large group of patients with brain lesions. METHODS: We analyzed fMRI data from a sequential retrospective cohort of 51 patients with brain lesions who underwent presurgical fMRI language mapping. We compared the effectiveness of three language tasks (Antonym Generation, Sentence Completion (SC), and Auditory Naming) for lateralizing language function and for activating cortex within patient-specific regions-of-interest representing eloquent language areas, and assessed the degree of spatial overlap of the areas of activation elicited by each task. RESULTS: The tasks were similarly effective for lateralizing language within the anterior language areas. The SC task produced higher laterality indices within the posterior language areas and had a significantly higher agreement with the clinical report. Dice coefficients between the task pairs were in the range of .351-.458, confirming substantial variation in the components of the language network activated by each task. CONCLUSIONS: SC task consistently produced large activations within the dominant hemisphere and was more effective for lateralizing language within the posterior language areas. The low degree of spatial overlap among the tasks strongly supports the practice of using a battery of tasks to help the surgeon to avoid eloquent language areas.
Sankha S Basu, Madison H McMinn, Begoña Giménez-Cassina Lopéz, Michael S Regan, Elizabeth C Randall, Amanda R Clark, Christopher R Cox, and Nathalie YR Agar. 2019. “Metal Oxide Laser Ionization Mass Spectrometry Imaging (MOLI MSI) Using Cerium(IV) Oxide.” Anal Chem, 91, 10, Pp. 6800-7.Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful technique for spatially resolved metabolomics. A variation on MALDI, termed metal oxide laser ionization (MOLI), capitalizes on the unique property of cerium(IV) oxide (CeO) to induce laser-catalyzed fatty acyl cleavage from lipids and has been utilized for bacterial identification. In this study, we present the development and utilization of CeO as an MSI catalyst. The method was developed using a MALDI TOF instrument in negative ion mode, equipped with a high frequency laser. Instrument parameters for MOLI MS fatty acid catalysis with CeO were optimized with phospholipid standards and fatty acid catalysis was confirmed using lipid extracts from reference bacterial strains, and sample preparation was optimized using mouse brain tissue. MOLI MSI was applied to the imaging of normal mouse brain revealing differentiable fatty acyl pools in myelinated and nonmyelinated regions. Similarly, MOLI MSI showed distinct fatty acyl composition in tumor regions of a patient derived xenograft mouse model of glioblastoma. To assess the potential of MOLI MSI to detect pathogens directly from tissue, a pseudoinfection model was prepared by spotting Escherichia coli lipid extracts on mouse brain tissue sections and imaged by MOLI MSI. The spotted regions were molecularly resolved from the supporting mouse brain tissue by the diagnostic odd-chained fatty acids and reflected control bacterial MOLI MS signatures. We describe MOLI MSI for the first time and highlight its potential for spatially resolved fatty acyl analysis, characterization of fatty acyl composition in tumors, and its potential for pathogen detection directly from tissue.

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