A sparse intraoperative data-driven biomechanical model to compensate for brain shift during neuronavigation

A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality

Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.

Moving Particle Semi-Implicit and Finite Element Method Coupled Analysis for Brain Shift Estimation

Neuronavigation is a computer-assisted technique for presenting three-dimensional images of a patient’s brain to facilitate immediate and precise lesion localization by surgeons. Neuronavigation systems use preoperative medical images of patients. In neurosurgery, when the dura mater and arachnoid membrane are incised and the cerebrospinal fluid (CSF) drains out, the brain loses the CSF buoyancy and deforms in the direction of gravity, which is referred to as brain shift. This brain shift yields inaccurate neuronavigation. To reduce this inaccuracy, an intraoperative brain shift should be estimated. This paper proposes a dynamic simulation method for brain-shift estimation combining the moving-particle semi-implicit (MPS) method and the finite element method (FEM). The CSF was modeled using fluid particles, whereas the brain parenchyma was modeled using finite elements (FEs). Node particles were attached to the surface nodes of the brain parenchyma in the FE model. The interaction between the CSF and brain parenchyma was simulated using the repulsive force between the fluid particles and node particles. Validation experiments were performed using a gelatin block. The gelatin block was dipped into silicone oil, which was then gradually removed; the block deformation owing to the buoyancy loss was measured. The experimental deformation data were compared with the results of the MPS-FEM coupled analysis. The mean absolute error (MAE) between the simulated deformation and the average across the four experiments was 0.26 mm, while the mean absolute percentage error (MAPE) was 27.7%. Brain-shift simulations were performed using the MPS-FEM coupled analysis, and the computational cost was evaluated.

Catheter guided cerebral glioma resection combined with awake craniotomy: its usefulness and surgical outcome

Purpose: A challenging aspect of glioma surgery is to distinguish tumour tissue from surrounding eloquent structures and perform resection with accuracy. Various technologies have been used to address this issue including neuronavigator, intraoperative magnetic resonant imaging, intraoperative ultrasound, and fluorescence, each of which has certain drawbacks and limitations. In this study, authors demonstrate the technique of using stereotactically placed catheters as guidance during cerebral glioma resection and report the surgical outcomes. Materials and methods: This study included patients with intrinsic cerebral tumour adjacent to the eloquent structures. Catheter trajectories were planned using three-dimensional cerebral reconstruction on navigation software and catheters were stereotactically placed to mark the intended extent of resection. All craniotomies were performed in awake fashion under neurophysiologic mapping and continuous physical examination for safe maximal resection. Clinical outcome and intended versus actual extent of resection were analysed. Results: Between January 2015 and December 2016, 15 consecutive patients (8 males and 7 females) with intrinsic cerebral tumour underwent craniotomy with this technique. Median age was 43 years. Seven patients (46.7%) had worsening neurological status within 24 h postoperatively. Of these 7 patients, 6 patients (85.7%) regained preoperative neurological status by 6 months. The intended extent of resections were total, subtotal and partial in 3 (20%), 9 (60%), and 3 (20%) patients, respectively. The actual extent of resections were total, subtotal and partial in 3 (20%), 8(53.3%), and 4 (26.7%) patients, respectively. There were no catheter related complications. There was no 30-day postoperative mortality. Conclusions: Catheter guided resection along with awake surgery and neurophysiologic monitoring is a valid technique for infiltrative tumour, especially for ones locating near eloquent structures where the margin of error is low. This is a simple and economical technique which requires only standard equipment widely available to neurosurgical operating theatres.

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work.

Android application for determining surgical variables in brain-tumor resection procedures

The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient's preoperative image volume to more closely match the intraoperative state of the patient's brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient's neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.

A combined registration and finite element analysis method for fast estimation of intraoperative brain shift; phantom and animal model study

BACKGROUND

Finite element models for estimation of intraoperative brain shift suffer from huge computational cost. In these models, image registration and finite element analysis are two time-consuming processes.

METHODS

The proposed method is an improved version of our previously developed Finite Element Drift (FED) registration algorithm. In this work the registration process is combined with the finite element analysis. In the Combined FED (CFED), the deformation of whole brain mesh is iteratively calculated by geometrical extension of a local load vector which is computed by FED.

RESULTS

While the processing time of the FED-based method including registration and finite element analysis was about 70 s, the computation time of the CFED was about 3.2 s. The computational cost of CFED is almost 50% less than similar state of the art brain shift estimators based on finite element models.

CONCLUSIONS

The proposed combination of registration and structural analysis can make the calculation of brain deformation much faster.

Intraoperative image updating for brain shift following dural opening

OBJECTIVE Preoperative magnetic resonance images (pMR) are typically coregistered to provide intraoperative navigation, the accuracy of which can be significantly compromised by brain deformation. In this study, the authors generated updated MR images (uMR) in the operating room (OR) to compensate for brain shift due to dural opening, and evaluated the accuracy and computational efficiency of the process. METHODS In 20 open cranial neurosurgical cases, a pair of intraoperative stereovision (iSV) images was acquired after dural opening to reconstruct a 3D profile of the exposed cortical surface. The iSV surface was registered with pMR to detect cortical displacements that were assimilated by a biomechanical model to estimate whole-brain nonrigid deformation and produce uMR in the OR. The uMR views were displayed on a commercial navigation system and compared side by side with the corresponding coregistered pMR. A tracked stylus was used to acquire coordinate locations of features on the cortical surface that served as independent positions for calculating target registration errors (TREs) for the coregistered uMR and pMR image volumes. RESULTS The uMR views were visually more accurate and well aligned with the iSV surface in terms of both geometry and texture compared with pMR where misalignment was evident. The average misfit between model estimates and measured displacements was 1.80 ± 0.35 mm, compared with the average initial misfit of 7.10 ± 2.78 mm between iSV and pMR, and the average TRE was 1.60 ± 0.43 mm across the 20 patients in the uMR image volume, compared with 7.31 ± 2.82 mm on average in the pMR cases. The iSV also proved to be accurate with an average error of 1.20 ± 0.37 mm. The overall computational time required to generate the uMR views was 7-8 minutes. CONCLUSIONS This study compensated for brain deformation caused by intraoperative dural opening using computational model-based assimilation of iSV cortical surface displacements. The uMR proved to be more accurate in terms of model-data misfit and TRE in the 20 patient cases evaluated relative to pMR. The computational time was acceptable (7-8 minutes) and the process caused minimal interruption of surgical workflow.

Brain shift in neuronavigation of brain tumors: A review

PURPOSE

Neuronavigation based on preoperative imaging data is a ubiquitous tool for image guidance in neurosurgery. However, it is rendered unreliable when brain shift invalidates the patient-to-image registration. Many investigators have tried to explain, quantify, and compensate for this phenomenon to allow extended use of neuronavigation systems for the duration of surgery. The purpose of this paper is to present an overview of the work that has been done investigating brain shift.

METHODS

A review of the literature dealing with the explanation, quantification and compensation of brain shift is presented. The review is based on a systematic search using relevant keywords and phrases in PubMed. The review is organized based on a developed taxonomy that classifies brain shift as occurring due to physical, surgical or biological factors.

RESULTS

This paper gives an overview of the work investigating, quantifying, and compensating for brain shift in neuronavigation while describing the successes, setbacks, and additional needs in the field. An analysis of the literature demonstrates a high variability in the methods used to quantify brain shift as well as a wide range in the measured magnitude of the brain shift, depending on the specifics of the intervention. The analysis indicates the need for additional research to be done in quantifying independent effects of brain shift in order for some of the state of the art compensation methods to become useful.

CONCLUSION

This review allows for a thorough understanding of the work investigating brain shift and introduces the needs for future avenues of investigation of the phenomenon.

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases

PURPOSE

Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases.

METHODS

Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients.

RESULTS

The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm.

CONCLUSIONS

Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.

A nonrigid registration method for correcting brain deformation induced by tumor resection

PURPOSE

This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model.

METHODS

The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables.

RESULTS

To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics-based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points.

CONCLUSIONS

The proposed method can effectively correct brain deformation induced by tumor resection.

Augment low-field intra-operative MRI with preoperative MRI using a hybrid non-rigid registration method

BACKGROUND

Preoperatively acquired diffusion tensor image (DTI) and blood oxygen level dependent (BOLD) have been proved to be effective in providing more anatomical and functional information; however, the brain deformation induced by brain shift and tumor resection severely impairs the correspondence between the image space and the patient space in image-guided neurosurgery.

METHOD

To address the brain deformation, we developed a hybrid non-rigid registration method to register high-field preoperative MRI with low-field intra-operative MRI in order to recover the deformation induced by brain shift and tumor resection. The registered DTI and BOLD are fused with low-field intra-operative MRI for image-guided neurosurgery.

RESULTS

The proposed hybrid registration method was evaluated by comparing the landmarks predicted by the hybrid registration method with the landmarks identified in the low-field intra-operative MRI for 10 patients. The prediction error of the hybrid method is 1.92±0.54 mm, and the compensation accuracy is 74.3±5.0%. Compared to the landmarks far from the resection region, those near the resection region demonstrated a higher compensation accuracy (P-value=.003) although these landmarks had larger initial displacements.

CONCLUSIONS

The proposed hybrid registration method is able to bring preoperatively acquired BOLD and DTI into the operating room and compensate for the deformation to augment low-field intra-operative MRI with rich anatomical and functional information.

Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models

Conventional image-guided neurosurgery relies on preoperative images to provide surgical navigational information and visualization. However, these images are no longer accurate once the skull has been opened and brain shift occurs. To account for changes in the shape of the brain caused by mechanical (e.g., gravity-induced deformations) and physiological effects (e.g., hyperosmotic drug-induced shrinking, or edema-induced swelling), updated images of the brain must be provided to the neuronavigation system in a timely manner for practical use in the operating room. In this paper, a novel preoperative and intraoperative computational processing pipeline for near real-time brain shift correction in the operating room was developed to automate and simplify the processing steps. Preoperatively, a computer model of the patient's brain with a subsequent atlas of potential deformations due to surgery is generated from diagnostic image volumes. In the case of interim gross changes between diagnosis, and surgery when reimaging is necessary, our preoperative pipeline can be generated within one day of surgery. Intraoperatively, sparse data measuring the cortical brain surface is collected using an optically tracked portable laser range scanner. These data are then used to guide an inverse modeling framework whereby full volumetric brain deformations are reconstructed from precomputed atlas solutions to rapidly match intraoperative cortical surface shift measurements. Once complete, the volumetric displacement field is used to update, i.e., deform, preoperative brain images to their intraoperative shifted state. In this paper, five surgical cases were analyzed with respect to the computational pipeline and workflow timing. With respect to postcortical surface data acquisition, the approximate execution time was 4.5 min. The total update process which included positioning the scanner, data acquisition, inverse model processing, and image deforming was ~11-13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future.

Preliminary study of a novel method for conveying corrected image volumes in surgical navigation

BACKGROUND

Commercial image-guided surgery systems rely on the fundamental assumption that preoperative medical images represent the physical state of the patient in the operating room. The guidance display typically consists of a three-dimensional (3D) model derived from medical images and three orthogonal views of the imaging data. A challenging question in image-guided surgery is: what happens when the images used in the guidance display no longer correspond to the current geometric state of the anatomy and guidance information is still desirable?

METHODS

We modify the conventional display with two techniques for incorporating a displacement field from a finite-element model into the guidance display and present a preliminary study of the effect of our method on performance with a simple surgical task. The topic of this paper is methods for conveying the computational model solution, not the model itself. To address the integration of the computational model solution into the display, a novel method of applying the deformation to the tool tip was developed, which quickly corrects for deformation but also maintains the pristine nature of the preoperative images. We compare the proposed technique to an existing method that applies the deformation field to the image volume.

RESULTS

A pilot study compared mean performance with our method of applying the deformation to the tool tip and the conventional technique. These methods were statistically similar with respect to accuracy of localization (p < 0.05) and amount of time (p < 0.05) required for localization of the target.

CONCLUSIONS

These results suggest that our new technique can be used in place of the computationally expensive task of deforming the image volume, without affecting the time or accuracy of the surgical task. Most notably, our work addresses the problem of incorporating deformation correction into the guidance display and offers a first step toward understanding its effect on surgical performance.

Special surgical considerations for functional brain mapping

The development of functional mapping techniques gives neurosurgeons many options for preoperative planning. Integrating functional and anatomic data can inform patient selection and surgical planning and makes functional mapping more accessible than when only invasive studies were available. However, the applications of functional mapping to neurosurgical patients are still evolving. Functional imaging remains complex and requires an understanding of the underlying physiologic and imaging characteristics. Neurosurgeons must be accustomed to interpreting highly processed data. Successful implementation of functional image-guided procedures requires efficient interactions between neurosurgeon, neurologist, radiologist, neuropsychologist, and others, but promises to enhance the care of patients.