Herz C, MacNeil K, Behringer PA, Tokuda J, Mehrtash A, Mousavi P, Kikinis R, Fennessy FM, Tempany CM, Tuncali K, et al. Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy. IEEE Trans Biomed Eng. 2020;67 (2) :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.
Zong S, Shen G, Mei C-S, Madore B. Improved PRF-Based MR Thermometry Using k-Space Energy Spectrum Analysis. Magn Reson Med. 2020.Abstract
PURPOSE: Proton resonance frequency (PRF) thermometry encodes information in the phase of MRI signals. A multiplicative factor converts phase changes into temperature changes, and this factor includes the TE. However, phase variations caused by B and/or B inhomogeneities can effectively change TE in ways that vary from pixel to pixel. This work presents how spatial phase variations affect temperature maps and how to correct for corresponding errors. METHODS: A method called "k-space energy spectrum analysis" was used to map regions in the object domain to regions in the k-space domain. Focused ultrasound heating experiments were performed in tissue-mimicking gel phantoms under two scenarios: with and without proper shimming. The second scenario, with deliberately de-adjusted shimming, was meant to emulate B inhomogeneities in a controlled manner. The TE errors were mapped and compensated for using k-space energy spectrum analysis, and corrected results were compared with reference results. Furthermore, a volunteer was recruited to help evaluate the magnitude of the errors being corrected. RESULTS: The in vivo abdominal results showed that the TE and heating errors being corrected can readily exceed 10%. In phantom results, a linear regression between reference and corrected temperatures results provided a slope of 0.971 and R of 0.9964. Analysis based on the Bland-Altman method provided a bias of -0.0977°C and 95% limits of agreement that were 0.75°C apart. CONCLUSION: Spatially varying TE errors, such as caused by B and/or B inhomogeneities, can be detected and corrected using the k-space energy spectrum analysis method, for increased accuracy in proton resonance frequency thermometry.
Levitt JJ, Nestor PG, Kubicki M, Lyall AE, Zhang F, Riklin-Raviv T, O Donnell LJ, McCarley RW, Shenton ME, Rathi Y. Miswiring of Frontostriatal Projections in Schizophrenia. Schizophr Bull. 2020.Abstract
We investigated brain wiring in chronic schizophrenia and healthy controls in frontostriatal circuits using diffusion magnetic resonance imaging tractography in a novel way. We extracted diffusion streamlines in 27 chronic schizophrenia and 26 healthy controls connecting 4 frontal subregions to the striatum. We labeled the projection zone striatal surface voxels into 2 subtypes: dominant-input from a single cortical subregion, and, functionally integrative, with mixed-input from diverse cortical subregions. We showed: 1) a group difference for total striatal surface voxel number (P = .045) driven by fewer mixed-input voxels in the left (P  = .007), but not right, hemisphere; 2) a group by hemisphere interaction for the ratio quotient between voxel subtypes (P  = .04) with a left (P  = .006), but not right, hemisphere increase in schizophrenia, also reflecting fewer mixed-input voxels; and 3) fewer mixed-input voxel counts in schizophrenia (P  = .045) driven by differences in left hemisphere limbic (P  = .007) and associative (P  = .01), but not sensorimotor, striatum. These results demonstrate a less integrative pattern of frontostriatal structural connectivity in chronic schizophrenia. A diminished integrative pattern yields a less complex input pattern to the striatum from the cortex with less circuit integration at the level of the striatum. Further, as brain wiring occurs during early development, aberrant brain wiring could serve as a developmental biomarker for schizophrenia.
Zhang F, Noh T, Juvekar P, Frisken SF, Rigolo L, Norton I, Kapur T, Pujol S, Wells W, Yarmarkovich A, et al. SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization. JCO Clin Cancer Inform. 2020;4 :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.
Senders JT, Staples P, Mehrtash A, Cote DJ, Taphoorn MJB, Reardon DA, Gormley WB, Smith TR, Broekman ML, Arnaout O. An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning. Neurosurgery. 2020;86 (2) :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 ( 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.
Yao S, Lin P, Vera M, Akter F, Zhang R-Y, Zeng A, Golby AJ, Xu G, Tie Y, Song J. Hormone Levels are Related to Functional Compensation in Prolactinomas: A Resting-state fMRI Study. J Neurol Sci. 2020;411 :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.
Randall EC, Lopez BGC, Peng S, Regan MS, Abdelmoula WM, Basu SS, Santagata S, Yoon H, Haigis MC, Agar JN, et al. Localized Metabolomic Gradients in Patient-Derived Xenograft Models of Glioblastoma. Cancer Res. 2020;80 (6) :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..
Shono N, Ninni B, King F, Kato T, Tokuda J, Fujimoto T, Tuncali K, Hata N. Simulated Accuracy Assessment of Small Footprint Body-mounted Probe Alignment Device for MRI-guided Cryotherapy of Abdominal Lesions. Med Phys. 2020.Abstract
PURPOSE: Magnetic resonance imaging (MRI)-guided percutaneous cryotherapy of abdominal lesions, an established procedure, uses MRI to guide and monitor the cryoablation of lesions. Methods to precisely guide cryotherapy probes with a minimum amount of trial-and-error are yet to be established. To aid physicians in attaining precise probe alignment without trial-and-error, a body-mounted motorized cryotherapy-probe alignment device (BMCPAD) with motion compensation was clinically tested in this study. The study also compared the contribution of body motion and organ motion compensation to the guidance accuracy of a body-mounted probe alignment device. METHODS: The accuracy of guidance using the BMCPAD was prospectively measured during MRI-guided percutaneous cryotherapies before insertion of the probes. Clinical parameters including patient age, types of anesthesia, depths of the target, and organ sites of target were collected. By using MR images of the target organs and fiducial markers embedded in the BMCPAD, we retrospectively simulated the guidance accuracy with body motion compensation, organ motion compensation, and no compensation. The collected data were analyzed to test the impact of motion compensation on the guidance accuracy. RESULTS: Thirty-seven physical guidance of probes were prospectively recorded for sixteen completed cases. The accuracy of physical guidance using the BMCPAD was 13.4 ± 11.1 mm. The simulated accuracy of guidance with body motion compensation, organ motion compensation, and no compensation was 2.4 ± 2.9 mm, 2.2 ± 1.6 mm, and 3.5 ± 2.9 mm, respectively. Data analysis revealed that the body motion compensation and organ motion compensation individually impacted the improvement in the accuracy of simulated guidance. Moreover, the difference in the accuracy of guidance either by body motion compensation or organ motion compensation was not statistically significant. The major clinical parameters impacting the accuracy of guidance were the body and organ motions. Patient age, types of anesthesia, depths of the target, and organ sites of target did not influence the accuracy of guidance using BMCPAD. The magnitude of body surface movement and organ movement exhibited mutual statistical correlation. CONCLUSIONS: The BMCPAD demonstrated guidance accuracy comparable to that of previously reported devices for CT-guided procedures. The analysis using simulated motion compensation revealed that body motion compensation and organ motion compensation individually impact the improvement in the accuracy of device-guided cryotherapy probe alignment. Considering the correlation between body and organ movements, we also determined that body motion compensation using the ring fiducial markers in the BMCPAD can be solely used to address both body and organ motions in MRI-guided cryotherapy.
Bunevicius A, McDannold NJ, Golby AJ. Focused Ultrasound Strategies for Brain Tumor Therapy. Oper Neurosurg (Hagerstown). 2020;19 (1) :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.
Freedman RA, Gelman RS, Agar NYR, Santagata S, Randall EC, Gimenez-Cassina Lopez B, Connolly RM, Dunn IF, Van Poznak CH, Anders CK, et al. Pre- and Postoperative Neratinib for HER2-Positive Breast Cancer Brain Metastases: Translational Breast Cancer Research Consortium 022. Clin Breast Cancer. 2020;20 (2) :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 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.
Zhou H, Jagadeesan J. Real-Time Dense Reconstruction of Tissue Surface From Stereo Optical Video. IEEE Trans Med Imaging. 2020;39 (2) :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.
Yao S, Liebenthal E, Juvekar P, Bunevicius A, Vera M, Rigolo L, Golby AJ, Tie Y. Sex Effect on Presurgical Language Mapping in Patients With a Brain Tumor. Front Neurosci. 2020;14 :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.
Tokuda J, Wang Q, Tuncali K, Seethamraju RT, Tempany CM, Schmidt EJ. Temperature-Sensitive Frozen-Tissue Imaging for Cryoablation Monitoring Using STIR-UTE MRI. Invest Radiol. 2020;55 (5) :310-7.Abstract
PURPOSE: The aim of this study was to develop a method to delineate the lethally frozen-tissue region (temperature less than -40°C) arising from interventional cryoablation procedures using a short tau inversion-recovery ultrashort echo-time (STIR-UTE) magnetic resonance (MR) imaging sequence. This method could serve as an intraprocedural validation of the completion of tumor ablation, reducing the number of local recurrences after cryoablation procedures. MATERIALS AND METHODS: The method relies on the short T1 and T2* relaxation times of frozen soft tissue. Pointwise Encoding Time with Radial Acquisition, a 3-dimensional UTE sequence with TE = 70 microseconds, was optimized with STIR to null tissues with a T1 of approximately 271 milliseconds, the threshold T1. Because the T1 relaxation time of frozen tissue in the temperature range of -40°C < temperature < -8°C is shorter than the threshold T1 at the 3-tesla magnetic field, tissues in this range should appear hyperintense. The sequence was evaluated in ex vivo frozen tissue, where image intensity and actual tissue temperatures, measured by thermocouples, were correlated. Thereafter, the sequence was evaluated clinically in 12 MR-guided prostate cancer cryoablations, where MR-compatible cryoprobes were used to destroy cancerous tissue and preserve surrounding normal tissue. RESULTS: The ex vivo experiment using a bovine muscle demonstrated that STIR-UTE images showed regions approximately between -40°C and -8°C as hyperintense, with tissues at lower and higher temperatures appearing dark, making it possible to identify the region likely to be above the lethal temperature inside the frozen tissue. In the clinical cases, the STIR-UTE images showed a dark volume centered on the cryoprobe shaft, Vinner, where the temperature is likely below -40°C, surrounded by a doughnut-shaped hyperintense volume, where the temperature is likely between -40°C and -8°C. The hyperintense region was itself surrounded by a dark volume, where the temperature is likely above -8°C, permitting calculation of Vouter. The STIR-UTE frozen-tissue volumes, Vinner and Vouter, appeared significantly smaller than signal voids on turbo spin echo images (P < 1.0 × 10), which are currently used to quantify the frozen-tissue volume ("the iceball"). The ratios of the Vinner and Vouter volumes to the iceball were 0.92 ± 0.08 and 0.29 ± 0.07, respectively. In a single postablation follow-up case, a strong correlation was seen between Vinner and the necrotic volume. CONCLUSIONS: Short tau inversion-recovery ultrashort echo-time MR imaging successfully delineated the area approximately between -40°C and -8°C isotherms in the frozen tissue, demonstrating its potential to monitor the lethal ablation volume during MR-guided cryoablation.
Leung LWL, Unadkat P, Bertotti MM, Bi WL, Essayed WI, Bunevicius A, Chavakula V, Rigolo L, Fumagalli L, Tie Z, et al. Clinical Utility of Preoperative Bilingual Language fMRI Mapping in Patients with Brain Tumors. J Neuroimaging. 2020;30 (2) :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.
Xie G, Zhang F, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, et al. Anatomical Assessment of Trigeminal Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition B-Values and Single- and Multi-Fiber Tracking Strategies. Neuroimage Clin. 2020;25 :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.
Yamada A, Tokuda J, Naka S, Murakami K, Tani T, Morikawa S. Magnetic Resonance and Ultrasound Image-guided Navigation System using a Needle Manipulator. Med Phys. 2020;47 (3) :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.
Rigolo L, Essayed WI, Tie Y, Norton I, Mukundan S, Golby A. Intraoperative Use of Functional MRI for Surgical Decision Making after Limited or Infeasible Electrocortical Stimulation Mapping. J Neuroimaging. 2020;30 (2) :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.
Bunevicius A, Schregel K, Sinkus R, Golby A, Patz S. REVIEW: MR Elastography of Brain Tumors. Neuroimage Clin. 2020;25 :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.
Cheng C-C, Preiswerk F, Madore B. Multi-pathway Multi-echo Acquisition and Neural Contrast Translation to Generate a Variety of Quantitative and Qualitative Image Contrasts. Magn Reson Med. 2020;83 (6) :2310-21.Abstract
PURPOSE: Clinical exams typically involve acquiring many different image contrasts to help discriminate healthy from diseased states. Ideally, 3D quantitative maps of all of the main MR parameters would be obtained for improved tissue characterization. Using data from a 7-min whole-brain multi-pathway multi-echo (MPME) scan, we aimed to synthesize several 3D quantitative maps (T and T ) and qualitative contrasts (MPRAGE, FLAIR, T -weighted, T -weighted, and proton density [PD]-weighted). The ability of MPME acquisitions to capture large amounts of information in a relatively short amount of time suggests it may help reduce the duration of neuro MR exams. METHODS: Eight healthy volunteers were imaged at 3.0T using a 3D isotropic (1.2 mm) MPME sequence. Spin-echo, MPRAGE, and FLAIR scans were performed for training and validation. MPME signals were interpreted through neural networks for predictions of different quantitative and qualitative contrasts. Predictions were compared to reference values at voxel and region-of-interest levels. RESULTS: Mean absolute errors (MAEs) for T and T maps were 216 ms and 11 ms, respectively. In ROIs containing white matter (WM) and thalamus tissues, the mean T /T predicted values were 899/62 ms and 1139/58 ms, consistent with reference values of 850/66 ms and 1126/58 ms, respectively. For qualitative contrasts, signals were normalized to those of WM, and MAEs for MPRAGE, FLAIR, T -weighted, T -weighted, and PD-weighted contrasts were 0.14, 0.15, 0.13, 0.16, and 0.05, respectively. CONCLUSIONS: Using an MPME sequence and neural-network contrast translation, whole-brain results were obtained with a variety of quantitative and qualitative contrast in ~6.8 min.
Frisken S, Luo M, Juvekar P, Bunevicius A, Machado I, Unadkat P, Bertotti MM, Toews M, Wells WM, Miga MI, et al. 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. 2020;15 (1) :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.