Publications

2020
Aida Steiner, Gabriela Alban, Teresa Cheng, Tina Kapur, Camden Bay, Pierre-Yves McLaughlin, Martin King, Clare Tempany, and Larissa J Lee. 4/2020. “Vaginal Recurrence of Endometrial Cancer: MRI Characteristics and Correlation With Patient Outcome After Salvage Radiation Therapy.” Abdom Radiol (NY), 45, 4, Pp. 1122-31.Abstract
PURPOSE: To evaluate MRI characteristics in vaginal recurrence of endometrial cancer (EC) including tumor volume shrinkage during salvage radiotherapy, and to identify imaging features associated with survival. METHODS: Patients with vaginal recurrence of EC treated with external beam radiotherapy (EBRT) followed by brachytherapy (BT), and with available pelvic MRI at two time points: baseline and/or before BT were retrospectively identified from 2004 to 2017. MRI features including recurrence location and tissue characteristics on T2- and T1-weighted images were evaluated at baseline only. Tumor volumes were measured both at baseline and pre-BT. Survival rates and associations were evaluated by Cox regression and Fisher's exact test, respectively. RESULTS: Sixty-two patients with 36 baseline and 50 pre-BT pelvic MRIs were included (24/62 with both MRIs). Vaginal recurrence of EC was most commonly located in the vaginal apex (27/36, 75%). Tumors with a post-contrast enhancing peripheral rim or low T2 signal rim at baseline showed longer recurrence-free survival (RFS) (HR 0.2, 95% CI 0.1-0.9, P < 0.05 adjusted for histology; HR 0.2, 95% CI 0.1-0.8, P < 0.05, respectively). The median tumor shrinkage at pre-BT was 69% (range 1-99%). Neither absolute tumor volumes nor volume regression at pre-BT were associated with RFS. Lymphovascular space invasion (LVSI) at hysterectomy and adjuvant RT were associated with recurrence involving the distal vagina (both P < 0.05). CONCLUSION: Vaginal recurrences with rim enhancement at baseline MRI predicted improved RFS, while tumor volume shrinkage at pre-BT did not. Distal vaginal recurrence was more common in patients with LVSI and adjuvant RT at EC diagnosis.
Lok Wa Laura Leung, Prashin Unadkat, Melina More Bertotti, Wenya Linda Bi, Walid Ibn Essayed, Adomas Bunevicius, Vamsidhar Chavakula, Laura Rigolo, Luca Fumagalli, Ziyun Tie, Alexandra J Golby, and Yanmei Tie. 3/2020. “Clinical Utility of Preoperative Bilingual Language fMRI Mapping in Patients with Brain Tumors.” J Neuroimaging, 30, 2, Pp. 175-83.Abstract
BACKGROUND AND PURPOSE: Previous literature has demonstrated disparity in the postoperative recovery of first and second language function of bilingual neurosurgical patients. However, it is unclear to whether preoperative brain mapping of both languages is needed. In this study, we aimed to evaluate the clinical utility of language task functional MRI (fMRI) implemented in both languages in bilingual patients. METHODS: We retrospectively examined fMRI data of 13 bilingual brain tumor patients (age: 23 to 59 years) who performed antonym generation task-based fMRIs in English and non-English language. The usefulness of bilingual language mapping was evaluated using a structured survey administered to 5 neurosurgeons. Additionally, quantitative comparison between the brain activation maps of both languages was performed. RESULTS: Survey responses revealed differences in raters' surgical approach, including asleep versus awake surgery and extent of resection, after viewing the language fMRI maps. Additional non-English fMRI led to changes in surgical decision-making and bettered localization of language areas. Quantitative analysis revealed an increase in laterality index (LI) in non-English fMRI compared to English fMRI. The Dice coefficient demonstrated fair overlap (.458 ± .160) between the activation maps. CONCLUSION: Bilingual fMRI mapping of bilingual patients allows to better appreciate functionally active language areas that may be neglected in single language mapping. Utility of bilingual mapping was supported by changes in both surgical approach and LI measurements, suggesting its benefit on preoperative language mapping.
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..
Atsushi Yamada, Junichi Tokuda, Shigeyuki Naka, Koichiro Murakami, Tohru Tani, and Shigehiro Morikawa. 3/2020. “Magnetic Resonance and Ultrasound Image-guided Navigation System using a Needle Manipulator.” Med Phys, 47, 3, Pp. 850-8.Abstract
PURPOSE: Image guidance is crucial for percutaneous tumor ablations, enabling accurate needle-like applicator placement into target tumors while avoiding tissues that are sensitive to injury and/or correcting needle deflection. Although ultrasound (US) is widely used for image guidance, magnetic resonance (MR) is preferable due to its superior soft tissue contrast. The objective of this study was to develop and evaluate an MR and US multi-modal image-guided navigation system with a needle manipulator to enable US-guided applicator placement during MR imaging (MRI)-guided percutaneous tumor ablation. METHODS: The MRI-compatible needle manipulator with US probe was installed adjacent to a 3 Tesla MRI scanner patient table. Coordinate systems for the MR image, patient table, manipulator, and US probe were all registered using an optical tracking sensor. The patient was initially scanned in the MRI scanner bore for planning and then moved outside the bore for treatment. Needle insertion was guided by real-time US imaging fused with the reformatted static MR image to enhance soft tissue contrast. Feasibility, targeting accuracy, and MR compatibility of the system were evaluated using a bovine liver and agar phantoms. RESULTS: Targeting error for 50 needle insertions was 1.6 ± 0.6 mm (mean ± standard deviation). The experiment confirmed that fused MR and US images provided real-time needle localization against static MR images with soft tissue contrast. CONCLUSIONS: The proposed MR and US multi-modal image-guided navigation system using a needle manipulator enabled accurate needle insertion by taking advantage of static MR and real-time US images simultaneously. Real-time visualization helped determine needle depth, tissue monitoring surrounding the needle path, target organ shifts, and needle deviation from the path.
Fan Zhang, Thomas Noh, Parikshit Juvekar, Sarah F Frisken, Laura Rigolo, Isaiah Norton, Tina Kapur, Sonia Pujol, William Wells, Alex Yarmarkovich, Gordon Kindlmann, Demian Wassermann, Raul San Jose Estepar, Yogesh Rathi, Ron Kikinis, Hans J Johnson, Carl-Fredrik Westin, Steve Pieper, Alexandra J Golby, and Lauren J O'Donnell. 3/2020. “SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization.” JCO Clin Cancer Inform, 4, Pp. 299-309.Abstract
PURPOSE: We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS: In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS: We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION: SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.
Yuanqian Gao, Kiyoshi Takagi, Takahisa Kato, Naoyuki Shono, and Nobuhiko Hata. 2/2020. “Continuum Robot With Follow-the-Leader Motion for Endoscopic Third Ventriculostomy and Tumor Biopsy.” IEEE Trans Biomed Eng, 67, 2, Pp. 379-90.Abstract
BACKGROUND: In a combined endoscopic third ventriculostomy (ETV) and endoscopic tumor biopsy (ETB) procedure, an optimal tool trajectory is mandatory to minimize trauma to surrounding cerebral tissue. OBJECTIVE: This paper presents wire-driven multi-section robot with push-pull wire. The robot is tested to attain follow-the-leader (FTL) motion to place surgical instruments through narrow passages while minimizing the trauma to tissues. METHODS: A wire-driven continuum robot with six sub-sections was developed and its kinematic model was proposed to achieve FTL motion. An accuracy test to assess the robot's ability to attain FTL motion along a set of elementary curved trajectory was performed. We also used hydrocephalus ventricular model created from human subject data to generate five ETV/ETB trajectories and conducted a study assessing the accuracy of the FTL motion along these clinically desirable trajectories. RESULTS: In the test with elementary curved paths, the maximal deviation of the robot was increased from 0.47 mm at 30 turn to 1.78 mm at 180 in a simple C-shaped curve. S-shaped FTL motion had lesser deviation ranging from 0.16 to 0.18 mm. In the phantom study, the greatest tip deviation was 1.45 mm, and the greatest path deviation was 1.23 mm. CONCLUSION: We present the application of a continuum robot with FTL motion to perform a combined ETV/ETB procedure. The validation study using human subject data indicated that the accuracy of FTL motion is relatively high. The study indicated that FTL motion may be useful tool for combined ETV and ETB.
Christian Wachinger, Matthew Toews, Georg Langs, William Wells, and Polina Golland. 2/2020. “Keypoint Transfer for Fast Whole-Body Segmentation.” IEEE Trans Med Imaging, 39, 2, Pp. 273-82.Abstract
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified distinctive image locations, where each keypoint correspondence suggests a transformation between images. We use these correspondences to transfer label maps of entire organs from the training images to the test image. The keypoint transfer algorithm includes three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ segmentations. We report segmentation results for abdominal organs in whole-body CT and MRI, as well as in contrast-enhanced CT and MRI. Our method offers a speed-up of about three orders of magnitude in comparison to common multi-atlas segmentation, while achieving an accuracy that compares favorably. Moreover, keypoint transfer does not require the registration to an atlas or a training phase. Finally, the method allows for the segmentation of scans with highly variable field-of-view.
Michael P Catalino, Shun Yao, Deborah L Green, Edward R Laws, Alexandra J Golby, and Yanmei Tie. 2/2020. “Mapping Cognitive and Emotional Networks in Neurosurgical Patients Using Resting-State Functional Magnetic Resonance Imaging.” Neurosurg Focus, 48, 2, Pp. 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.
Joeky T Senders, Patrick Staples, Alireza Mehrtash, David J Cote, Martin JB Taphoorn, David A Reardon, William B Gormley, Timothy R Smith, Marike L Broekman, and Omar Arnaout. 2/2020. “An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.” Neurosurgery, 86, 2, Pp. E184-E192.Abstract
BACKGROUND: Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata. OBJECTIVE: To compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator. METHODS: Patients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves. RESULTS: In total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/). CONCLUSION: The development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.
Christian Herz, Kyle MacNeil, Peter A Behringer, Junichi Tokuda, Alireza Mehrtash, Parvin Mousavi, Ron Kikinis, Fiona M Fennessy, Clare M Tempany, Kemal Tuncali, and Andriy Fedorov. 2/2020. “Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy.” IEEE Trans Biomed Eng, 67, 2, Pp. 565-76.Abstract
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
Brittany M Stopa, Joeky T Senders, Marike LD Broekman, Mark Vangel, and Alexandra J Golby. 2/2020. “Preoperative Functional MRI Use in Neurooncology Patients: A Clinician Survey.” Neurosurg Focus, 48, 2, Pp. 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.
Haoyin Zhou and Jayender Jagadeesan. 2/2020. “Real-Time Dense Reconstruction of Tissue Surface From Stereo Optical Video.” IEEE Trans Med Imaging, 39, 2, Pp. 400-12.Abstract
We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time, the basic idea of which is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. To handle the common low-texture regions on tissue surfaces, we propose effective post-processing steps for the local stereo matching method to enlarge the radius of constraint, which include outliers removal, hole filling, and smoothing. Since the tissue models obtained by stereo matching are limited to the field of view of the imaging modality, we propose a model mosaicking method by using a novel feature-based simultaneously localization and mapping (SLAM) method to align the models. Low-texture regions and the varying illumination condition may lead to a large percentage of feature matching outliers. To solve this problem, we propose several algorithms to improve the robustness of the SLAM, which mainly include 1) a histogram voting-based method to roughly select possible inliers from the feature matching results; 2) a novel 1-point RANSAC-based [Formula: see text] algorithm called as DynamicR1PP [Formula: see text] to track the camera motion; and 3) a GPU-based iterative closest points (ICP) and bundle adjustment (BA) method to refine the camera motion estimation results. Experimental results on ex- and in vivo data showed that the reconstructed 3D models have high-resolution texture with an accuracy error of less than 2 mm. Most algorithms are highly parallelized for GPU computation, and the average runtime for processing one key frame is 76.3 ms on stereo images with 960×540 resolution.
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.
Sarah Frisken, Ma Luo, Parikshit Juvekar, Adomas Bunevicius, Ines Machado, Prashin Unadkat, Melina M Bertotti, Matt Toews, William M Wells, Michael I Miga, and Alexandra J Golby. 1/2020. “A Comparison of Thin-Plate Spline Deformation and Finite Element Modeling to Compensate for Brain Shift during Tumor Resection.” Int J Comput Assist Radiol Surg, 15, 1, Pp. 75-85.Abstract
PURPOSE: Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons' ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data. METHODS: Data acquired during 19 sequential tumor resections in Brigham and Women's Hospital's Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation. RESULTS: The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods. CONCLUSIONS: In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.
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.
Shun Yao, Einat Liebenthal, Parikshit Juvekar, Adomas Bunevicius, Matthew Vera, Laura Rigolo, Alexandra J Golby, and Yanmei Tie. 1/2020. “Sex Effect on Presurgical Language Mapping in Patients With a Brain Tumor.” Front Neurosci, 14, Pp. 4.Abstract
Differences between males and females in brain development and in the organization and hemispheric lateralization of brain functions have been described, including in language. Sex differences in language organization may have important implications for language mapping performed to assess, and minimize neurosurgical risk to, language function. This study examined the effect of sex on the activation and functional connectivity of the brain, measured with presurgical functional magnetic resonance imaging (fMRI) language mapping in patients with a brain tumor. We carried out a retrospective analysis of data from neurosurgical patients treated at our institution who met the criteria of pathological diagnosis (malignant brain tumor), tumor location (left hemisphere), and fMRI paradigms [sentence completion (SC); antonym generation (AG); and resting-state fMRI (rs-fMRI)]. Forty-seven patients (22 females, mean age = 56.0 years) were included in the study. Across the SC and AG tasks, females relative to males showed greater activation in limited areas, including the left inferior frontal gyrus classically associated with language. In contrast, males relative to females showed greater activation in extended areas beyond the classic language network, including the supplementary motor area (SMA) and precentral gyrus. The rs-fMRI functional connectivity of the left SMA in the females was stronger with inferior temporal pole (TP) areas, and in the males with several midline areas. The findings are overall consistent with theories of greater reliance on specialized language areas in females relative to males, and generalized brain areas in males relative to females, for language function. Importantly, the findings suggest that sex could affect fMRI language mapping. Thus, considering sex as a variable in presurgical language mapping merits further investigation.
2019
Karol Miller, Grand R Joldes, George Bourantas, Simon K Warfield, Damon E Hyde, Ron Kikinis, and Adam Wittek. 10/2019. “Biomechanical Modeling and Computer Simulation of the Brain during Neurosurgery.” Int J Numer Method Biomed Eng, 35, 10, Pp. e3250.Abstract
Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
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.

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