Image-guided Neurosurgery Research Articles

Open access segmentations of intraoperative brain tumor ultrasound images.

Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques forneurosurgery. The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, sulci, falx cerebri, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations. Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk. The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance inneurosurgery.

HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications

PurposeMagnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI.MethodsThis work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient’s facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area.ResultsUsing the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient’s face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 ± 0.19 mm and a target registration error of 4.07 ± 1.28 mm, respectively.ConclusionsThis work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.

Noctopus: a novel device and method for patient registration and navigation in image-guided cranial surgery.

A patient registration and real-time surgical navigation system and a novel device and method (Noctopus) is presented. With any tracking system technology and a patient/target-specific registration marker configuration, submillimetric target registration error (TRE), high-precise application accuracy for single or multiple anatomical targets in image-guided neurosurgery or ENT surgery is realized. The system utilizes the advantages of marker-based registration technique and allows to perform automatized patient registration using on the device attached and with patient scanned four fiducial markers. The best possible sensor/marker positions around the patient's head are determined for single or multiple region(s) of interest (target/s) in the anatomy. Once brought at the predetermined positions the device can be operated with any tracking system for registration purposes. Targeting accuracy was evaluated quantitatively at various target positions on a phantom skull. The target registration error (TRE) was measured on individual targets using an electromagnetic tracking system. The overall averaged TRE was 0.22 ± 0.08 mm for intraoperative measurements. An automatized patient registration system using optimized patient-/target-specific marker configurations is proposed. High-precision and user-error-free intraoperative surgical navigation with minimum number of registration markers and sensors is realized. The targeting accuracy is significantly improved in minimally invasive neurosurgical and ENT interventions.

NeuroIGN: Explainable Multimodal Image-Guided System for Precise Brain Tumor Surgery.

Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes.

Multimodal Neuroimaging Computing: Basics and Applications in Neurosurgery.

In neurosurgery, multimodal neuroimaging computing plays a critical role by providing a comprehensive and detailed understanding of the brain and its function. This integrated approach can unlock deeper insights into complex neurological diseases, as well as providing a big picture for image-guided neurosurgery and precision medicine. In this chapter, we will introduce the recent updates of neuroimaging techniques, their applications in neurosurgery scenarios, the difficulties of data processing and computing, and potential future perspectives.

A deep unrolled neural network for real-time MRI-guided brain intervention

Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI). Here, we proposed a deep unrolled neural network, dubbed as LSFP-Net, for real-time i-MRI reconstruction. By integrating LSFP-Net and a custom-designed, MR-compatible interventional device into a 3 T MRI scanner, a real-time MRI-guided brain intervention system is proposed. The performance of the system was evaluated using phantom and cadaver studies. 2D/3D real-time i-MRI was achieved with temporal resolutions of 80/732.8 ms, latencies of 0.4/3.66 s including data communication, processing and reconstruction time, and in-plane spatial resolution of 1 × 1 mm2. The results demonstrated that the proposed method enables real-time monitoring of the remote-controlled brain intervention, and showed the potential to be readily integrated into diagnostic scanners for image-guided neurosurgery.

Comparison of physics-based deformable registration methods for image-guided neurosurgery.

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration.

Towards Estimating MRI-Ultrasound Registration Error in Image-Guided Neurosurgery.

Image-guided neurosurgery allows surgeons to view their tools in relation to pre-operatively acquired patient images and models. To continue using neuronavigation systems throughout operations, image registration between pre-operative images (typically MRI) and intra-operative images (e.g., ultrasound) are common to account for brain shift (deformations of the brain during surgery). We implemented a method to estimate MRI-ultrasound registration errors, with the goal of enabling surgeons to quantitatively assess the performance of linear or nonlinear registrations. To the best of our knowledge, this is the first dense error estimating algorithm applied to multimodal image registrations. The algorithm is based on a previously proposed sliding-window convolutional neural network that operates on a voxel-wise basis. To create training data where the true registration error is known, simulated ultrasound images were created from pre-operative MRI images and artificially deformed. The model was evaluated on artificially deformed simulated ultrasound data as well as real ultrasound data with manually annotated landmark points. The model achieved a mean absolute error of 0.977 ± 0.988 mm and correlation of 0.8 ± 0.062 on the simulated ultrasound data, and a mean absolute error of 2.24 ± 1.89 mm and a correlation of 0.246 on the real ultrasound data. We discuss concrete areas to improve the results on real ultrasound data. Our progress lays the foundation for future developments and ultimately implementation on clinical neuronavigation systems.

SEEGAtlas: A framework for the identification and classification of depth electrodes using clinical images

Objective. Accurate localization, classification, and visualization of intracranial electrodes are fundamental for analyzing intracranial electrographic recordings. While manual contact localization is the most common approach, it is time-consuming, prone to errors, and is particularly challenging and subjective in low quality images, which are common in clinical practice. Automatically locating and interactively visualizing where each of the 100–200 individual contacts records in the brain is essential for understanding the neural origins of intracranial EEG. Approach. We introduced the SEEGAtlas plugin for the IBIS system, an open-source software platform for image-guided neurosurgery and multi-modal image visualization. SEEGAtlas extends IBIS functionalities to semi-automatically locate depth-electrode contact coordinates and automatically label the tissue type and anatomical region in which each contact is located. To illustrate the capabilities of SEEGAtlas and to validate the algorithms, clinical magnetic resonance images (MRIs) before and after electrode implantation of ten patients with depth electrodes implanted to localize the origin of their epileptic seizures were analyzed. Main Results. Visually identified contact coordinates were compared with the coordinates obtained by SEEGAtlas, resulting in a median difference of 1.4 mm. The agreement was lower for MRIs with weak susceptibility artifacts than for high-quality images. The tissue type was classified with 86% agreement with visual inspection. The anatomical region was classified as having a median agreement across patients of 82%. Significance. The SEEGAtlas plugin is user-friendly and enables accurate localization and anatomical labeling of individual contacts along implanted electrodes, together with powerful visualization tools. Employing the open-source SEEGAtlas results in accurate analysis of the recorded intracranial electroencephalography (EEG), even when only suboptimal clinical imaging is available. A better understanding of the cortical origin of intracranial EEG would help improve clinical interpretation and answer fundamental questions of human neuroscience.

A robust motion correction technique for infrared thermography during awake craniotomy

PurposeIntraoperative infrared thermography is an emerging technique for image-guided neurosurgery, whereby physiological and pathological processes result in temperature changes over space and time. However, motion during data collection leads to downstream artifacts in thermography analyses. We develop a fast, robust technique for motion estimation and correction as a preprocessing step for brain surface thermography recordings.MethodsA motion correction technique for thermography was developed which approximates the deformation field associated with motion as a grid of two-dimensional bilinear splines (Bispline registration), and a regularization function was designed to constrain motion to biomechanically feasible solutions. The performance of the proposed Bispline registration technique was compared to phase correlation, a band-stop filter, demons registration, and the Horn–Schunck and Lucas–Kanade optical flow techniques.ResultsAll methods were analyzed using thermography data from ten patients undergoing awake craniotomy for brain tumor resection, and performance was compared using image quality metrics. The proposed method had the lowest mean-squared error and the highest peak-signal-to-noise ratio of all methods tested and performed slightly worse than phase correlation and Demons registration on the structural similarity index metric (p < 0.01, Wilcoxon signed-rank test). Band-stop filtering and the Lucas–Kanade method were not strong attenuators of motion, while the Horn–Schunck method was well-performing initially but weakened over time.ConclusionBispline registration had the most consistently strong performance out of all the techniques tested. It is relatively fast for a nonrigid motion correction technique, capable of processing ten frames per second, and could be a viable option for real-time use. Constraining the deformation cost function through regularization and interpolation appears sufficient for fast, monomodal motion correction of thermal data during awake craniotomy.

Using virtual lines of navigation for a successful transcortical approach.

Neuronavigation systems have become essential tools in image-guided neurosurgery that aid in the accurate resection of brain tumors. Recent advancements to these devices can indicate the precise location of lesions but can also project an augmented reality (AR) image on the microscope eyepiece to facilitate a successful surgical operation. Although the transcortical approach is a very popular method in neurosurgery, it can lead to disorientation and can cause unnecessary brain damage when the distance from the brain surface to the lesion is long. Herein, we report on an actual case in which a virtual line from AR images was used to assist the transcortical approach. A virtual line connecting the entry point and the target point, which were set as the navigation route, was created using Stealth station S7® (Medtronic, Minneapolis, USA). This line was projected as an AR image on the microscope eyepiece. It was possible to reach the target point by proceeding through the white matter along the displayed virtual line. The lesion was reached within a short duration using virtual line without disorientation. Setting a virtual line as an AR image using neuronavigation is a simple and accurate method that can effectively support the conventional transcortical approach.

Drilling accuracy evaluation of a mouldable surgical targeting system for minimally invasive access to anatomic targets in the temporal bone.

Minimally invasive cochlear implant surgery using a micro-stereotactic surgical targeting system with on-site moulding of the template aims for a reliable, less experience-dependent access to the inner ear under maximal reduction of trauma to anatomic structures. We present an accuracy evaluation of our system in ex-vivo testing. Eleven drilling experiments were performed on four cadaveric temporal bone specimens. The process involved preoperative imaging after affixing the reference frame to the skull, planning of a safe trajectory preserving relevant anatomical structures, customization of the surgical template, execution of the guided drilling and postoperative imaging for determination of the drilling accuracy. Deviation between the drilled and desired trajectories was measured at different depths. All drilling experiments were successfully performed. Other than purposely sacrificing the chorda tympani in one experiment, no other relevant anatomy, such as facial nerve, chorda tympani, ossicles or external auditory canal were harmed. Deviation between the desired and achieved path was found to be 0.25 ± 0.16mm at skulls' surface and 0.51 ± 0.35mm at the target level. The closest distance of the drilled trajectories' outer circumference to the facial nerve was 0.44mm. We demonstrated the usability for drilling to the middle ear on human cadaveric specimen in a pre-clinical setting. Accuracy proved to be suitable for many applications such as procedures within the field of image-guided neurosurgery. Promising approaches to reach sufficient submillimetre accuracy for CI surgery have been outlined.

Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.

Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention. To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality. The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network (GAN) with a conditional loss function modulated by aleatoric uncertainty was developed for CBCT-to-CT synthesis. Epistemic uncertainty of the synthesis model was estimated via Monte Carlo (MC) dropout. Using spatially varying weights derived from epistemic uncertainty, the DL-Recon image combines the synthetic CT with an artifact-corrected filtered back-projection (FBP) reconstruction. In regions of high epistemic uncertainty, DL-Recon includes greater contribution from the FBP image. Twenty paired real CT and simulated CBCT images of the head were used for network training and validation, and experiments evaluated the performance of DL-Recon on CBCT images containing simulated and real brain lesions not present in the training data. Performance among learning- and physics-based methods was quantified in terms of structural similarity (SSIM) of the resulting image to diagnostic CT and Dice similarity metric (DSC) in lesion segmentation compared to ground truth. A pilot study was conducted involving seven subjects with CBCT images acquired during neurosurgery to assess the feasibility of DL-Recon in clinical data. CBCT images reconstructed via FBP with physics-based corrections exhibited the usual challenges to soft-tissue contrast resolution due to image non-uniformity, noise, and residual artifacts. GAN synthesis improved image uniformity and soft-tissue visibility but was subject to error in the shape and contrast of simulated lesions that were unseen in training. Incorporation of aleatoric uncertainty in synthesis loss improved estimation of epistemic uncertainty, with variable brain structures and unseen lesions exhibiting higher epistemic uncertainty. The DL-Recon approach mitigated synthesis errors while maintaining improvement in image quality, yielding 15%-22% increase in SSIM (image appearance compared to diagnostic CT) and up to 25% increase in DSC in lesion segmentation compared to FBP. Clear gains in visual image quality were also observed in real brain lesions and in clinical CBCT images. DL-Recon leveraged uncertainty estimation to combine the strengths of DL and physics-based reconstruction and demonstrated substantial improvements in the accuracy and quality of intraoperative CBCT. The improved soft-tissue contrast resolution could facilitate visualization of brain structures and support deformable registration with preoperative images, further extending the utility of intraoperative CBCT in image-guided neurosurgery.

A Neuronavigation System Using a Mobile Augmented Reality Solution

Image-guided surgery has shown great utility in neurosurgery, especially in allowing for more accurate surgical planning and navigation. The current gold standard for image-guided neurosurgery is neuronavigation, which provides millimetric accuracy on such tasks. However, these approaches often require a complicated setup and have high cost, hindering their potential in low- and middle-income countries. The aim of this study was to develop and evaluate the performance of a mobile-based augmented reality neuronavigation solution under different conditions in a preclinical environment. The application was developed using the Swift programming language and was tested on a replica of a human scalp under variable lighting, with different numbers of registration points and target point position conditions. For each condition, reference points were input into the application, and the target points were computed for 10 iterations. The mean registration error and target error were used to assess the performance of the application. In the best-case scenario, the proposed solution had a mean target error of 2.6 ± 1.6 mm. Our approach provides a viable, low-cost, easy-to-use, portable method for locating points on the scalp surface with an accuracy of 2.6 ± 1.6 mm in the best-case scenario.

A Low-Power and Wide Dynamic Range Digital Pixel Sensor for Intrinsic Optical Imaging in Image-Guided Neurosurgery and Neocortical Epilepsy

Purpose: Determining the borders of brain tumors, seizure foci, and their elimination in patients with brain tumors is very important in preventing cancer recurrence. Multispectral Optical Intrinsic Signal Imaging (MS-OISI) image-guided neurosurgery using visible-Near-Infrared (NIR) wavelengths have shown great potential for image-guided neurosurgery. One of the main challenges is the need for low-power consumption, high-speed and above 100dB dynamic range to capture both visible and NIR photons. The overarching goal of this work is to create a Digital Pixel Sensor (DPS) as a Complementary Metal-Oxide-Semiconductor (CMOS) camera for MS-OISI of the brain in image-guided neurosurgery that has a wide dynamic range, low power consumption, and high speed.&#x0D; Materials and Methods: The general view of neurosurgical system, DPS, and circuit operation of conventional Pulsed Frequency Modulation (PFM) DPS are given first. The proposed PFM DPS and circuit implementation are shown, as well as simulation results obtained using a circuit simulator. Finally, a comparison with other similar works is given.&#x0D; Results: The proposed pixel simulation results show that the performance parameters such as dynamic range and power consumption has improved in comparison to similar works. However, due to its complicated circuitry, it has a low spatial resolution, which can be compensated.&#x0D; Conclusion: The image sensor is post-layout simulated in 0.18μm CMOS technology with a 1.3V supply voltage, resulting in 140dB dynamic range and 7.69μW power dissipation with a 11% fill factor. The key novelty for the proposed PFM DPS is: in-pixel Analog-to-Digital Conversion (ADC), using a low voltage and high-speed dynamic comparator. Furthermore, this work uses a self-reset mechanism, which eliminates the need for an external pulse source, as well as variable reference voltage, which eliminates the necessity for a global constant reference voltage. All of these features demonstrate the excellent potential of the proposed pixel for MS-OISI image-guided neurosurgery.

The Current Status in Intraoperative Image-Guided Neurosurgery Associated with Progressive Operating Rooms: A Retrospective Analysis

Shinshu University Hospital has advanced operating rooms including a mobile computed tomography (mCT) room, Smart Cyber Operating Theater (SCOT) with intraoperative magnetic resonance imaging, hybrid operating room (hOR) with intraoperative image-guided surgery, and conventional operating rooms. We investigated the characteristics of cases assigned to each operating room. Five hundred forty neurosurgery cases from January 2018 to April 2021 were analyzed. We analyzed the selection of operating room according to pathology, surgical device requirement, and urgency, and we examined associations between operating room characteristics and these factors. Neurological surgeries were performed in an mCT room, an hOR, a SCOT, and a conventional operating room in 333 (61.7%), 64 (11.9%), 49 (9.1%), and 94 (17.4%) cases, respectively. mCT rooms were more frequently selected than other rooms for vascular/extra-axial tumors, which have a lower need for intraoperative image guidance. Spinal surgeries with segment diagnosis or intraoperative bone removal tended to be performed in the hOR. The rate of SCOT use tended to be higher for intra-axial tumors with poorly circumscribed borders than for vascular/extra-axial tumors. Endoscopic procedures were more frequently performed in the SCOT and mCT rooms than in hORs and conventional operating rooms. Emergency surgeries were often performed in the conventional operating rooms, even in cases where SCOT and hOR seemed suitable. Intraoperative image-guided surgeries were performed according to the characteristics of each operating room best suited for various diseases and operative methods. Further research is needed to prove whether operating room selection improves neurosurgical outcomes.

Global optimization point-set registration based on translation/rotation decoupling for image-guided surgery applications.

In image-guided surgery systems, image-to-patient spatial registration is to get the spatial transformation between the image space and the actual operating space. Although the image-to-patient spatial registration methods using paired point or surface matching are used in some image-guided neurosurgery systems, the key problem is that the global optimization registration result cannot be achieved. Therefore, this paper proposes a new rotation invariant feature for decoupling rotation and translation space, based on which global optimization point set registration method is proposed. The new rotation invariant features, constructed based on the edges and the angles, are the rotation invariant, which has high feature resolution. Some of them are not only the rotation invariant, but also the translation invariant. To obtain the global optimal solution, branch and bound search strategy is used to search the parameter space of the translation and the computational cost is reduced simultaneously. The registration accuracy of the spatial registration method is analyzed by comparing the difference between the estimated transform and the standard transform to calculate the registration error. To validate the performance of the spatial registration method proposed, the registration performance was analyzed by comparing the experimental results with the results of the two mainstream registration methods (the iterative closest point [ICP] registration method and the coherent point drift method). In the experiments, the comparison was based on the registration accuracy and the execution time. We show our registration method can obtain higher accuracy in a shorter time in most cases. At the same time, when using ICP to further refine our results, the ICP method can converge in a very short time, which also shows that our method provides a good initial pose for the ICP method and can help the ICP converge to the global optimal solution faster. Our method can achieve an average rotation error of 0.124 degrees and an average translation error of 0.38 mm on 10 clinical data. The results reveal that the surface registration method based on translation rotation decoupling can achieve superior performance regarding both the registration accuracy and the time efficiency in the image-to-patient spatial registration.

Facial landmark-guided surface matching for image-to-patient registration with an RGB-D camera.

Fiducial marker-based image-to-patient registration is the most common way in image-guided neurosurgery, which is labour-intensive, time consuming, invasive and error prone. We proposed a method of facial landmark-guided surface matching for image-to-patient registration using an RGB-D camera. Five facial landmarks are localised from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi-scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB-D data is initialised by aligning these five landmarks and further refined by weighted iterative closest point algorithm. Phantom experiment results show the target registration error is less than 3mm when the distance from the camera to the phantom is less than 1000mm. The registration takes less than 10s. The proposed method is comparable to the state-of-the-arts in terms of the accuracy yet more time-saving and non-invasive.

Augmented Reality for Enhancing Image-Guided Neurosurgery: Superimposing the Future onto the Present

Augmented Reality for Enhancing Image-Guided Neurosurgery: Superimposing the Future onto the Present

Inverse dynamic finite element-optimization modeling of the brain tumor mass-effect using a variable pressure boundary

Background and ObjectiveStatistical atlases of brain structure can potentially contribute in the surgical and radiotherapeutic treatment planning for the brain tumor patients. However, the current brain image-registration methods lack of accuracy when it comes to the mass-effect caused by tumor growth. Numerical simulations, such as finite element method (FEM), allow us to calculate the resultant pressure and deformation in the brain tissue due to tumor growth, and to predict the mass-effect. To date, however, the pressure boundary in the brain tissue due to tumor growth has been simply presented as a constant profile throughout the entire tumor outer surface that resulted in discrepancy between the patient imaging data and brain atlases. MethodsIn this study, we employed a fully-coupled inverse dynamic FE-optimization method to estimate the resultant variable pressure boundary due to tumor resection surgery. To do that, magnetic resonance imaging data of two patients’ pre- and post-tumor resection surgery were registered, segmented, volume-meshed, and prepared for fully-coupled inverse dynamic FE-optimization simulations. Two different pressure boundaries were defined on the brain cavity after tumor resection including: a) a constant pressure boundary and b) a variable pressure boundary. The inverse FE-optimization algorithm was used to find the optimum constant and variable pressure boundaries that result in the least distance between the surface-nodes of the post-surgery brain cavity and pre-surgery tumor. ResultsThe results revealed that a variable pressure boundary causes a considerably lower mean percentage error compared to a constant pressure one; hence, it can more effectively address the realistic boundary in tumor resection surgery and predict the mass-effect. ConclusionsThe proposed variable pressure boundary can be a robust tool that allows batch processing to register the brains with tumors to statistical atlases of normal brains and construction of brain tumor atlases. This approach is also computationally inexpensive and can be coupled to any FE software to run. The findings of this study have implications for not only predicting the accurate pressure boundary and mass-effect before tumor resection surgery, but also for predicting some clinical symptoms of brain cancers and presenting useful tools for APPLICATIONs in image-guided neurosurgery.