Brain Tumor Resection

The patients who benefit the most from AMIGO are in whom the distinction between tumor and brain and between different critical brain regions is most difficult. This occurs particularly in patients who have low grade gliomas, because although visible on certain imaging sequences, these tumors are nearly indistinguishable from surrounding brain during surgery even through the operating microscope. Moreover, visually all of the cortex and white matter look the same, and the surgeon can not discern the presence of white matter tracts or important areas during resection. In the AMIGO suite we can tie together preoperative mapping, accomplish intraoperative electrophysiology mapping, and obtain new US, MR, CT and PET images as needed.

Brain Tumor Resection Workflow in AMIGO

Craniotomy. Image guidance is used to perform a minimal craniotomy with optimized exposure of the lesion.
Ultrasound. When the dura is exposed, US is performed prior to making any incisions. US provides a fast initial orientation, including the location of major blood vessels. On left, the surgeon is using the BrainLab navigation system integrated with the BK US, and on the right is US with color doppler mode.
ECS. In a very small subset of cases, after the dura is opened and the cortex is exposed, intracranial electrical stimulation testing (ECS) is performed. ECS uses voltage applied directly to the cortex to map important functional areas. This is valuable in confirming and applying preoperative fMRI findings.
Navigation. Throughout the procedure, the pre-operative multimodal image data is used to navigate.
Navigation. Information is available to the surgeon about location and trajectory of her tools.
Tractography. Visualization of the tumor relative to the arcuate fasciculus white matter tract.
Gross Mass Removal. Gross tumor removal is performed using conventional tools aided by iterative neuro navigation. A cauterizer is shown in the picture.
Gross Mass Removal. Ablation and aspiration of tissue is shown.
Gross Mass Removal. Image guidance makes effective tumor resection possible.
Intraoperative MR. Prior to intraoperative MRI, a temporary closure is performed.
Intraoperative MR. A ceiling mounted high field (3T) MR scanner is then brought into the OR.
Tumor assessment. The tumor is contoured in green on the pre-op MRI image.
Assessment of residual tumor. The residual tumor is contoured in red.
Closure and post operative confirmatory imaging. Once the surgeon is satisfied with the extent of tumor resection, the dura is stitched, skull plate replaced, and skin stitched. Post-operative MR scans are obtained to confirm that there are no intraoperative complications and to set a new baseline. Once conscious, the patient is immediately asked to demonstrate motor control, such as foot movement. This confirms that resection has not affected at-risk areas of the motor cortex.

Book

  • Golby, Alexandra J, ed. Image-Guided Neurosurgery. 1st ed. Vol. Image-Guided Neurosurgery. Academic Press, 2015. p. 536. Print.

Book Chapters

  • Ferenc A. Jolesz, Alexandra J. Golby, Daniel A. Orringer. Magnetic Resonance Image-Guided Neurosurgery. Ch.32. Part V. Image-Guided Clinical Applications. In Ferenc A. Jolesz (Ed.), Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer; 2014. pp. 451-64.
  • Isaiah H. Norton, Daniel A. Orringer, Alexandra J. Golby. Image-Guided Neurosurgical Planning. Ch.37. Part V. Image-Guided Clinical Applications. In Ferenc A. Jolesz (Ed.), Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer; 2014. pp. 507-18.
  • Nobuhiko Hata, Paul R. Morrison, Zsolt Cselik, Ron Kikinis, Peter McL. Black, and Ferenc A. Jolesz. MRI-Guided and Controlled Laser-Induced Interstitial Thermal Therapy of Brain Tumors Using Integrated Navigation and Thermal Mapping Ch.42. Part V. Image-Guided Clinical Applications. In Ferenc A. Jolesz (Ed.), Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer; 2014. pp. 567-74.

Select Publications

Noh T, Mustroph M, Golby AJ. Intraoperative Imaging for High-Grade Glioma Surgery. Neurosurg Clin N Am. 2021;32 (1) :47-54.Abstract
This article discusses intraoperative imaging techniques used during high-grade glioma surgery. Gliomas can be difficult to differentiate from surrounding tissue during surgery. Intraoperative imaging helps to alleviate problems encountered during glioma surgery, such as brain shift and residual tumor. There are a variety of modalities available all of which aim to give the surgeon more information, address brain shift, identify residual tumor, and increase the extent of surgical resection. The article starts with a brief introduction followed by a review of with the latest advances in intraoperative ultrasound, intraoperative MRI, and intraoperative computed tomography.
Canalini L, Klein J, Miller D, Kikinis R. Enhanced Registration of Ultrasound Volumes by Segmentation of Resection Cavity in Neurosurgical Procedures. Int J Comput Assist Radiol Surg. 2020;15 (12) :1963-74.Abstract
PURPOSE: Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process. METHODS: The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity. RESULTS: Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm. CONCLUSIONS: We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery.
Narasimhan S, Weis JA, Luo M, Simpson AL, Thompson RC, Miga MI. Accounting for Intraoperative Brain Shift Ascribable to Cavity Collapse During Intracranial Tumor Resection. J Med Imaging (Bellingham). 2020;7 (3) :031506.Abstract
For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.
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.
Canalini L, Klein J, Miller D, Kikinis R. Segmentation-based Registration of Ultrasound Volumes for Glioma Resection in Image-guided Neurosurgery. Int J Comput Assist Radiol Surg. 2019;14 (10) :1697-1713.Abstract
PURPOSE: In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. METHODS: We chose to segment sulci and falx cerebri in US volumes, which remain visible during resection. To automatically segment these elements, first we trained a convolutional neural network on manually annotated structures in volumes acquired before the opening of the dura mater and then we applied it to segment corresponding structures in different surgical phases. Finally, the obtained masks are used to register US volumes acquired at multiple resection stages. RESULTS: Our method reduces the mean target registration error (mTRE) between volumes acquired before the opening of the dura mater and during resection from 3.49 mm (± 1.55 mm) to 1.36 mm (± 0.61 mm). Moreover, the mTRE between volumes acquired before opening the dura mater and at the end of the resection is reduced from 3.54 mm (± 1.75 mm) to 2.05 mm (± 1.12 mm). CONCLUSION: The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.
Luo M, Frisken SF, Weis JA, Clements LW, Unadkat P, Thompson RC, Golby AJ, Miga MI. Retrospective Study Comparing Model-Based Deformation Correction to Intraoperative Magnetic Resonance Imaging for Image-Guided Neurosurgery. J Med Imaging (Bellingham). 2017;4 (3) :035003.Abstract
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
Sastry R, Bi WL, Pieper S, Frisken S, Kapur T, Wells W, Golby AJ. Applications of Ultrasound in the Resection of Brain Tumors. J Neuroimaging. 2017;27 (1) :5-15.Abstract

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.

Incekara F, Olubiyi O, Ozdemir A, Lee T, Rigolo L, Golby A. The Value of Pre- and Intraoperative Adjuncts on the Extent of Resection of Hemispheric Low-Grade Gliomas: A Retrospective Analysis. J Neurol Surg A Cent Eur Neurosurg. 2016;77 (2) :79-87.Abstract

Background To achieve maximal resection with minimal risk of postoperative neurologic morbidity, different neurosurgical adjuncts are being used during low-grade glioma (LGG) surgery. Objectives To investigate the effect of pre- and intraoperative adjuncts on the extent of resection (EOR) of hemispheric LGGs. Methods Medical records were reviewed to identify patients of any sex, ≥ 18 years of age, who underwent LGG surgery at X Hospital between January 2005 and July 2013. Patients were divided into eight subgroups based on the use of various combinations of a neuronavigation system alone (NN), functional MRI-diffusion tensor imaging (fMRI-DTI) guided neuronavigation (FD), intraoperative MRI (MR), and direct electrical stimulation (DES). Initial and residual tumors were measured, and mean EOR was compared between groups. Results Of all 128 patients, gross total resection was achieved in 23.4%. Overall mean EOR was 81.3% ± 20.5%. Using DES in combination with fMRI-DTI (mean EOR: 86.7% ± 12.4%) on eloquent tumors improved mean EOR significantly after adjustment for potential confounders when compared with NN alone (mean EOR: 76.4% ± 25.5%; p = 0.001). Conclusions Using DES in combination with fMRI and DTI significantly improves EOR when LGGs are located in eloquent areas compared with craniotomies in which only NN was used.

Calligaris D, Feldman DR, Norton I, Brastianos PK, Dunn IF, Santagata S, Agar NYR. Molecular Typing of Meningiomas by Desorption Electrospray Ionization Mass Spectrometry Imaging for Surgical Decision-Making. Int J Mass Spectrom. 2015;377 :690-8.Abstract

Meningiomas are the most frequent intracranial tumors. The majority is benign slow-growing tumors but they can be difficult to treat depending on their location and size. While meningiomas are well delineated on magnetic resonance imaging by their uptake of contrast, surgical limitations still present themselves from not knowing the extent of invasion of the dura matter by meningioma cells. The development of tools to characterize tumor tissue in real or near real time could prevent recurrence after tumor resection by allowing for more precise surgery, i.e. removal of tumor with preservation of healthy tissue. The development of ambient ionization mass spectrometry for molecular characterization of tissue and its implementation in the surgical decision-making workflow carry the potential to fulfill this need. Here, we present the characterization of meningioma and dura mater by desorption electrospray ionization mass spectrometry to validate the technique for the molecular assessment of surgical margins and diagnosis of meningioma from surgical tissue in real-time. Nine stereotactically resected surgical samples and three autopsy samples were analyzed by standard histopathology and mass spectrometry imaging. All samples indicated a strong correlation between results from both techniques. We then highlight the value of desorption electrospray ionization mass spectrometry for the molecular subtyping/subgrouping of meningiomas from a series of forty genetically characterized specimens. The minimal sample preparation required for desorption electrospray ionization mass spectrometry offers a distinct advantage for applications relying on real-time information such as surgical decision-making. The technology here was tested to distinguish meningioma from dura mater as an approach to precisely define surgical margins. In addition we classify meningiomas into fibroblastic and meningothelial subtypes and more notably recognize meningiomas with NF2 genetic aberrations.

Calligaris D, Caragacianu D, Liu X, Norton I, Thompson CJ, Richardson AL, Golshan M, Easterling ML, Santagata S, Dillon DA, et al. Application of Desorption Electrospray Ionization Mass Spectrometry Imaging in Breast Cancer Margin Analysis. Proc Natl Acad Sci U S A. 2014;111 (42) :15184-9.Abstract

Distinguishing tumor from normal glandular breast tissue is an important step in breast-conserving surgery. Because this distinction can be challenging in the operative setting, up to 40% of patients require an additional operation when traditional approaches are used. Here, we present a proof-of-concept study to determine the feasibility of using desorption electrospray ionization mass spectrometry imaging (DESI-MSI) for identifying and differentiating tumor from normal breast tissue. We show that tumor margins can be identified using the spatial distributions and varying intensities of different lipids. Several fatty acids, including oleic acid, were more abundant in the cancerous tissue than in normal tissues. The cancer margins delineated by the molecular images from DESI-MSI were consistent with those margins obtained from histological staining. Our findings prove the feasibility of classifying cancerous and normal breast tissues using ambient ionization MSI. The results suggest that an MS-based method could be developed for the rapid intraoperative detection of residual cancer tissue during breast-conserving surgery.