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Brain shift during neurosurgery can compromise the fidelity of image guidance and potentially lead to surgical error. We have developed a finite element model-based brain shift compensation strategy to correct preoperative images for improved intraoperative navigation. This workflow-friendly approach precomputes potential intraoperative deformations (a ‘deformation atlas’) via a biphasic-biomechanical-model accounting for brain deformation associated with cerebrospinal fluid drainage, osmotic agents, resection, and swelling. Intraoperatively, an inverse problem approach is employed to provide a combinatory fit from the atlas that best matches sparse intraoperative measurements. Subsequently, preoperative image is deformed accordingly to better reflect patient’s intraoperative anatomy. While we have performed several retrospective studies examining model’s accuracy using post- or intra-operative magnetic resonance imaging, one challenging task is to examine model’s ability to recapture shift due to the aforementioned effects independently with clinical data and in a longitudinal manner under varying conditions. The work here is a case study where swelling was observed at the initial stage of surgery (after craniotomy and dura opening), subsequently sag was observed in a later stage of resection. Intraoperative tissue swelling and sag were captured via an optically tracked stylus by identifying cortical surface vessel features (n = 9), and model-based correction was performed for these two distinct types of brain shift at different stages of the procedure. Within the course of the entire surgery, we estimate the cortical surface experienced a deformation trajectory absolute path length of approximately 19.4 ± 2.1 mm reflecting swelling followed by sag. Overall, model reduced swelling-induced shift from 7.3 ± 1.1 to 1.8 ± 0.5 mm (~74.6% correction); for subsequent sag movement, model reduced shift from 6.4 ± 1.5 to 1.4 ± 0.5 mm (~76.6% correction).

Intra-operative soft tissue deformation, referred to as brain shift, compromises the application of current imageguided surgery (IGS) navigation systems in neurosurgery. A computational model driven by sparse data has been used as a cost effective method to compensate for cortical surface and volumetric displacements. Stereoscopic microscopes and laser range scanners (LRS) are the two most investigated sparse intra-operative imaging modalities for driving these systems. However, integrating these devices in the clinical workflow to facilitate development and evaluation requires developing systems that easily permit data acquisition and processing. In this work we present a mock environment developed to acquire stereo images from a tracked operating microscope and to reconstruct 3D point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space in order to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. Our experimental results report approximately 2mm average displacement error compared with the optical tracking system. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to LRS to collect sufficient intraoperative information for brain shift correction.

The quality of brain tumor resection surgery is dependent on the spatial agreement between preoperative image and intraoperative anatomy. However, brain shift compromises the aforementioned alignment. Currently, the clinical standard to monitor brain shift is intraoperative magnetic resonance (iMR). While iMR provides better understanding of brain shift, its cost and encumbrance is a consideration for medical centers. Hence, we are developing a model-based method that can be a complementary technology to address brain shift in standard resections, with resource-intensive cases as referrals for iMR facilities. Our strategy constructs a deformation ‘atlas’ containing potential deformation solutions derived from a biomechanical model that account for variables such as cerebrospinal fluid drainage and mannitol effects. Volumetric deformation is estimated with an inverse approach that determines the optimal combinatory ‘atlas’ solution fit to best match measured surface deformation. Accordingly, preoperative image is updated based on the computed deformation field. This study is the latest development to validate our methodology with iMR. Briefly, preoperative and intraoperative MR images of 2 patients were acquired. Homologous surface points were selected on preoperative and intraoperative scans as measurement of surface deformation and used to drive the inverse problem. To assess the model accuracy, subsurface shift of targets between preoperative and intraoperative states was measured and compared to model prediction. Considering subsurface shift above 3 mm, the proposed strategy provides an average shift correction of 59% across 2 cases. While further improvements in both the model and ability to validate with iMR are desired, the results reported are encouraging.

Brain shift compensation using computer modeling strategies is an important research area in the field of image-guided neurosurgery (IGNS). One important source of available sparse data during surgery to drive these frameworks is deformation tracking of the visible cortical surface. Possible methods to measure intra-operative cortical displacement include laser range scanners (LRS), which typically complicate the clinical workflow, and reconstruction of cortical surfaces from stereo pairs acquired with the operating microscopes. In this work, we propose and demonstrate a craniotomy simulation device that permits simulating realistic cortical displacements designed to measure and validate the proposed intra-operative cortical shift measurement systems. The device permits 3D deformations of a mock cortical surface which consists of a membrane made of a Dragon Skin® high performance silicone rubber on which vascular patterns are drawn. We then use this device to validate our stereo pair-based surface reconstruction system by comparing landmark positions and displacements measured with our systems to those positions and displacements as measured by a stylus tracked by a commercial optical system. Our results show a 1mm average difference in localization error and a 1.2mm average difference in displacement measurement. These results suggest that our stereo-pair technique is accurate enough for estimating intra-operative displacements in near real-time without affecting the surgical workflow.

Brain shift describes the deformation that the brain undergoes from mechanical and physiological effects typically during a neurosurgical or neurointerventional procedure. With respect to image guidance techniques, brain shift has been shown to compromise the fidelity of these approaches. In recent work, a computational pipeline has been developed to predict “brain shift” based on preoperatively determined surgical variables (such as head orientation), and subsequently correct preoperative images to more closely match the intraoperative state of the brain. However, a clinical workflow difficulty in the execution of this pipeline has been acquiring the surgical variables by the neurosurgeon prior to surgery. In order to simplify and expedite this process, an Android, Java-based application designed for tablets was developed to provide the neurosurgeon with the ability to orient 3D computer graphic models of the patient’s head, determine expected location and size of the craniotomy, and provide the trajectory into the tumor. These variables are exported for use as inputs for the biomechanical models of the preoperative computing phase for the brain shift correction pipeline. The accuracy of the application’s exported data was determined by comparing it to data acquired from the physical execution of the surgeon’s plan on a phantom head. Results indicated good overlap of craniotomy predictions, craniotomy centroid locations, and estimates of patient’s head orientation with respect to gravity. However, improvements in the app interface and mock surgical setup are needed to minimize error.

Using computational models, images acquired pre-operatively can be updated to account for intraoperative brain shift in image-guided surgical (IGS) systems. An optically tracked textured laser range scanner (tLRS) furnishes the 3D coordinates of cortical surface points (3D point clouds) over the surgical field of view and provides a correspondence between these and the pre-operative MR image. However, integration of the acquired tLRS data into a clinically acceptable system compatible throughout the clinical workflow of tumor resection has been challenging. This is because acquiring the tLRS data requires moving the scanner in and out of the surgical field, thus limiting the number of acquisitions. Large differences between acquisitions caused by tumor resection and tissue manipulation make it difficult to establish correspondence and estimate brain motion. An alternative to the tLRS is to use temporally dense feature-rich stereo surgical video data provided by the operating microscope. This allows for quick digitization of the cortical surface in 3D and can help continuously update the IGS system. In order to understand the tradeoffs between these approaches as input to an IGS system, we compare the accuracy of the 3D point clouds extracted from the stereo video system of the surgical microscope and the tLRS for phantom objects in this paper. We show that the stereovision system of the surgical microscope achieves accuracy in the 0.46-1.5mm range on our phantom objects and is a viable alternative to the tLRS for neurosurgical applications.

The use of biomechanical models to correct the misregistration due to deformation in image guided neurosurgical systems has been a growing area of investigation. In previous work, an atlas-based inverse model was developed to account for soft-tissue deformations during image-guided surgery. Central to that methodology is a considerable amount of pre-computation and planning. The goal of this work is to evaluate techniques that could potentially reduce that burden. Distinct from previous manual techniques, an automated segmentation technique is described for the cerebrum and dural septa. The shift correction results using this automated segmentation method were compared to those using the manual methods. In addition, the extent and distribution of the surgical parameters associated with the deformation atlas were investigated by a sensitivity analysis using simulation experiments and clinical data. The shift correction results did not change significantly using the automated method (correction of 73±13% ) as compared to the semi-automated method from previous work (correction of 76±13%). The results of the sensitivity analysis show that the atlas could be constructed by coarser sampling (six fold reduction) without substantial degradation in the shift reconstruction, a decrease in preoperative computational time from 13.1±3.5 hours to 2.2±0.6 hours. The automated segmentation technique and the findings of the sensitivity study have significant impact on the reduction of pre-operative computational time, improving the utility of the atlas-based method. The work in this paper suggests that the atlas-based technique can become a ‘time of surgery’ setup procedure rather than a pre-operative computing strategy.

Brain shift compromises the accuracy of neurosurgical image-guided interventions if not corrected by either intraoperative imaging or computational modeling. The latter requires intraoperative sparse measurements for constraining and driving model-based compensation strategies. Conoscopic holography, an interferometric technique that measures the distance of a laser light illuminated surface point from a fixed laser source, was recently proposed for non-contact surface data acquisition in image-guided surgery and is used here for validation of our modeling strategies. In this contribution, we use this inexpensive, hand-held conoscopic holography device for intraoperative validation of our computational modeling approach to correcting for brain shift. Laser range scan, instrument swabbing, and conoscopic holography data sets were collected from two patients undergoing brain tumor resection therapy at Vanderbilt University Medical Center. The results of our study indicate that conoscopic holography is a promising method for surface acquisition since it requires no contact with delicate tissues and can characterize the extents of structures within confined spaces. We demonstrate that for two clinical cases, the acquired conoprobe points align with our model-updated images better than the uncorrected images lending further evidence that computational modeling approaches improve the accuracy of image-guided surgical interventions in the presence of soft tissue deformations.

A patient specific finite element biphasic brain model has been utilized to codify a surgeon's experience by establishing quantifiable biomechanical measures to score orientations for optimal planning of brain tumor resection. When faced with evaluating several potential approaches to tumor removal during preoperative planning, the goal of this work is to facilitate the surgeon's selection of a patient head orientation such that tumor presentation and resection is assisted via favorable brain shift conditions rather than trying to allay confounding ones. Displacement-based measures consisting of area classification of the brain surface shifting in the craniotomy region and lateral displacement of the tumor center relative to an approach vector defined by the surgeon were calculated over a range of orientations and used to form an objective function. The objective function was used in conjunction with Levenberg-Marquardt optimization to find the ideal patient orientation. For a frontal lobe tumor presentation the model predicts an ideal orientation that indicates the patient should be placed in a lateral decubitus position on the side contralateral to the tumor in order to minimize unfavorable brain shift.

Biomechanical models that describe soft-tissue deformations provide a relatively inexpensive way to correct registration errors in image guided neurosurgical systems caused by non-rigid brain shifts. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based method have been developed recently which allow for uncertainty yet still capture the first order effects associated with brain deformations. More specifically, the technique involves building an atlas of solutions to account for the statistical uncertainty in factors that control the direction and magnitude of brain shift. The inverse solution is driven by a sparse intraoperative surface measurement. Since this subset of data only provides surface information, it could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies in intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The falx cerebri and tentorium cerebelli, two of the important dural septa, act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. The goals of this paper are to describe a novel method developed to segment the tentorium cerebelli, develop the procedure for modeling the dural septa and study the effect of those membranes on subsurface brain shift.

Measurement of intra-operative cortical brain movement is necessary to drive mechanical models developed to predict sub-cortical shift. At our institution, this is done with a tracked laser range scanner. This device acquires both 3D range data and 2D photographic images. 3D cortical brain movement can be estimated if 2D photographic images acquired over time can be registered. Previously, we have developed a method, which permits this registration using vessels visible in the images. But, vessel segmentation required the localization of starting and ending points for each vessel segment. Here, we propose a method, which automates the segmentation process further. This method involves several steps: (1) correction of lighting artifacts, (2) vessel enhancement, and (3) vessels' centerline extraction. Result obtained on 5 images obtained in the operating room suggests that our method is robust and is able to segment vessels reliably.

Often within the clinical environment of a neurosurgical brain tumor procedure, the surgeon is faced with the difficulty of orienting the patient's head to maximize the success of removing the pathology. Currently, these decisions are based on the experience of the surgeon. The primary objective of this paper is to demonstrate how a mathematical model can be used to evaluate the different patient positioning for tumor resection therapies. Specifically, therapies involving gravity-induced shift are used to demonstrate how a series of candidate approaches to the tumor can result in significantly different deformation behavior of brain tissue. To quantitatively assess the advantages and disadvantages of potential approaches, three different midline tumor locations were used to evaluate for the extent of tumor exposure and the magnitude of tensile stress at the brain-tumor interface, both of which are reliable indicators of the ease of resection. Preliminary results indicate that the lateral decubitus position is best suited for midline tumors.

In this paper, preliminary results from an image-to-physical space registration platform are presented. The current platform employs traditional and novel methods of registration which use a variety of data sources to include: traditional synthetic skin-fiducial point-based registration, surface registration based on facial contours, brain feature point-based registration, brain vessel-to-vessel registration, and a more comprehensive cortical surface registration method that utilizes both geometric and intensity information from both the image volume and physical patient. The intraoperative face and cortical surfaces were digitized using a laser range scanner (LRS) capable of producing highly resolved textured point clouds. In two in vivo cases, a series of registrations were performed using these techniques and compared within the context of a true target error. One of the advantages of using a textured point cloud data stream is that true targets among the physical cortical surface and the preoperative image volume can be identified and used to assess image-to-physical registration methods. The results suggest that iterative closest point (ICP) method for intraoperative face surface registration is equivalent to point-based registration (PBR) method of skin fiducial markers. With regard to the initial image and physical space registration, for patient 1, mean target registration error (TRE) were 3.1±0.4 mm and 3.6 ±0.9 mm for face ICP and skin fiducial PBR, respectively. For patient 2, the mean TRE were 5.7 ±1.3 mm, and 6.6 ±0.9 mm for face ICP and skin fiducial PBR, respectively. With regard to intraoperative cortical surface registration, SurfaceMI outperformed feature based PBR and vessel ICP with 1.7±1.8 mm for patient 1. For patient 2, the best result was achieved by using vessel ICP with 1.9±0.5 mm.

Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems (MUIGS). In this paper, we use sparse intraoperative data acquired with the help of a laser-range scanner and introduce a strategy for integrating this information with the computational model. The model solutions are computed preoperatively and are combined with the help of a statistical model to predict the intraoperative brain shift. Validation of this approach is performed with measured intraoperative data. The results indicate our ability to predict intraoperative brain shift to an accuracy of 1.3mm ± 0.7mm. This method appears to be a promising technique for increasing the speed and accuracy of MUIGS.

We have explored the capability of thermal imaging for the detection of brain tumors in a rat glioma mode. Fourteen Wistar rats were injected stereotactically with 100,000 C6 glioma cells. Approximately one and two weeks post implantation, the rats underwent bilateral craniotomy and the exposed brain surface was imaged with a short wave thermal camera. Thermal images were obtained at both low (approximately 28.7 degree(s)C) and high (approximately 38 degree(s)C) core temperatures. Temperature gradients between the tumor site and the contralateral normal brain were calculated. Overall, the tumors appeared cooler than normal brain, for both high and low core temperatures. Average temperature difference between tumor and normal brain were maximal in more advanced tumors (two weeks) and at higher core temperatures. At one week (N equals 6), the average temperature gradient between tumor and normal sites was 0.1 degree(s)C and 0.2 degree(s)C at low and high core temperatures respectively (P(greater than)0.05). At two weeks (N equals 8), the average temperature gradient was 0.3 degree(s)C and 0.7 degree(s)C at low and high core temperatures respectively (P

Fluorescence spectroscopy of the endogenous emission of brain tumors has been researched as a potentiallyimportant method for the intraoperative localization of brain tumor margins. In this study, we investigate theuse of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) for demarcation of primary braintumors by studying the time-resolved spectra of gliomas of different histologic grades. Time-resolvedfluorescence (3 ns, 337 nm excitation) from excised human brain tumor show differences between the time-resolved emission of malignant glioma and normal brain tissue (gray and white matter). Our findings suggestthat brain tumors can be differentiated from normal brain tissue based upon unique time-resolvedfluorescence signature. Keywords: Time-Resolved Laser Induced Fluorescence, brain tumor, glioma, spectroscopy.1. INTRODUCTIONDespite aggressive treatment including surgical resection, irradiation and chemotherapy, the median survivalof patients diagnosed with malignant gliomas is less than 12 months [1,2]. Surgical resection alone offers asurvival benefit, particularly when a complete resection can be achieved. Considerable evidence suggeststhat both survival and quality of life depend on the amount of tumor removed at surgery; a better prognosisoccurs when maximal surgical resection is achieved.The degree to which a complete resection can be carried out is limited in the brain by a number of factorsunique to the central nervous system. Primary brain tumors are infiltrating in nature, and the margins of thetumor are often indistinct. The goal at surgery is to remove as much of the tumor as safely possible withoutextending the resection into the normal adjacent brain [3]. Current imaging technologies to detect cancer inthe central nervous system fails to accurately detect the extent of infiltrating glial tumor [4,5]. Because

Time-Resolved Laser-Induced Fluorescence Spectroscopy (TR-LIFS) has the potential to provide a non- invasive characterization and detection of tumors. We utilized TR-LIFS to detect gliomas in-vivo in the rat C6 glioma model. Time-resolved emission spectra of both normal brain and tumor were analyzed to determine if unique fluorescence signatures could be used to distinguish the two. Fluorescence parameters derived from both spectral and time domain were used for tissue characterization. Our results show that in the rat C6 glioma model, TR-LIFS can be used to differentiate brain tumors from normal tissue (gray and white mater) based upon time- resolved fluorescence signatures seen in brain tumors.