Data assimilation using a gradient descent method for estimation of intraoperative brain deformation

Constrained estimation of intracranial aneurysm surface deformation using 4D-CTA

BACKGROUND AND OBJECTIVE

Intracranial aneurysms are relatively common life-threatening diseases, and assessing aneurysm rupture risk and identifying the associated risk factors is essential. Parameters such as the Oscillatory Shear Index, Pressure Loss Coefficient, and Wall Shear Stress are reliable indicators of intracranial aneurysm development and rupture risk, but aneurysm surface irregular pulsation has also received attention in aneurysm rupture risk assessment.

METHODS

The present paper proposed a new approach to estimate aneurysm surface deformation. This method transforms the estimation of aneurysm surface deformation into a constrained optimization problem, which minimizes the error between the displacement estimated by the model and the sparse data point displacements from the four-dimensional CT angiography (4D-CTA) imaging data.

RESULTS

The effect of the number of sparse data points on the results has been discussed in both simulation and experimental results, and it shows that the proposed method can accurately estimate the surface deformation of intracranial aneurysms when using sufficient sparse data points.

CONCLUSIONS

Due to a potential association between aneurysm rupture and surface irregular pulsation, the estimation of aneurysm surface deformation is needed. This paper proposed a method based on 4D-CTA imaging data, offering a novel solution for the estimation of intracranial aneurysm surface deformation.

Image Updating for Brain Shift Compensation During Resection

BACKGROUND

In open-cranial neurosurgery, preoperative magnetic resonance (pMR) images are typically coregistered for intraoperative guidance. Their accuracy can be significantly degraded by intraoperative brain deformation, especially when resection is involved.

OBJECTIVE

To produce model updated MR (uMR) images to compensate for brain shift that occurred during resection, and evaluate the performance of the image-updating process in terms of accuracy and computational efficiency.

METHODS

In 14 resection cases, intraoperative stereovision image pairs were acquired after dural opening and during resection to generate displacement maps of the surgical field. These data were assimilated by a biomechanical model to create uMR volumes of the evolving surgical field. A tracked stylus provided independent measurements of feature locations to quantify target registration errors (TREs) in the original coregistered pMR and uMR as surgery progressed.

RESULTS

Updated MR TREs were 1.66 ± 0.27 and 1.92 ± 0.49 mm in the 14 cases after dural opening and after partial resection, respectively, compared to 8.48 ± 3.74 and 8.77 ± 4.61 mm for pMR, respectively. The overall computational time for generating uMRs after partial resection was less than 10 min.

CONCLUSION

We have developed an image-updating system to compensate for brain deformation during resection using a computational model with data assimilation of displacements measured with intraoperative stereovision imaging that maintains TREs less than 2 mm on average.

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.

Improvement of Advanced Parkinson's Disease Manifestations with Deep Brain Stimulation of the Subthalamic Nucleus: A Single Institution Experience

We present our experience at the University of Illinois at Chicago (UIC) in deep brain stimulation (DBS) of the subthalamic nucleus (STN), describing our surgical technique, and reporting our clinical results, and morbidities. Twenty patients with advanced Parkinson’s disease (PD) who underwent bilateral STN-DBS were studied. Patients were assessed preoperatively and followed up for one year using the Unified Parkinson’s Disease Rating Scale (UPDRS) in “on” and “off” medication and “on” and “off” stimulation conditions. At one-year follow-up, we calculated significant improvement in all the motor aspects of PD (UPDRS III) and in activities of daily living (UPDRS II) in the “off” medication state. The “off” medication UPDRS improved by 49.3%, tremors improved by 81.6%, rigidity improved by 50.0%, and bradykinesia improved by 39.3%. The “off” medication UPDRS II scores improved by 73.8%. The Levodopa equivalent daily dose was reduced by 54.1%. The UPDRS IVa score (dyskinesia) was reduced by 65.1%. The UPDRS IVb score (motor fluctuation) was reduced by 48.6%. Deep brain stimulation of the STN improves the cardinal motor manifestations of the idiopathic PD. It also improves activities of daily living, and reduces medication-induced complications.

Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective

In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.

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.

Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases

OBJECT

Fiducial-based registration (FBR) is used widely for patient registration in image-guided neurosurgery. The authors of this study have developed an automatic fiducial-less registration (FLR) technique to find the patient-to-image transformation by directly registering 3D ultrasound (3DUS) with MR images without incorporating prior information. The purpose of the study was to evaluate the performance of the FLR technique when used prospectively in the operating room and to compare it with conventional FBR.

METHODS

In 32 surgical patients who underwent conventional FBR, preoperative T1-weighted MR images (pMR) with attached fiducial markers were acquired prior to surgery. After craniotomy but before dural opening, a set of 3DUS images of the brain volume was acquired. A 2-step registration process was executed immediately after image acquisition: 1) the cortical surfaces from pMR and 3DUS were segmented, and a multistart sum-of-squared-intensity-difference registration was executed to find an initial alignment between down-sampled binary pMR and 3DUS volumes; and 2) the alignment was further refined by a mutual information-based registration between full-resolution grayscale pMR and 3DUS images, and a patient-to-image transformation was subsequently extracted.

RESULTS

To assess the accuracy of the FLR technique, the following were quantified: 1) the fiducial distance error (FDE); and 2) the target registration error (TRE) at anterior commissure and posterior commissure locations; these were compared with conventional FBR. The results showed that although the average FDE (6.42 ± 2.05 mm) was higher than the fiducial registration error (FRE) from FBR (3.42 ± 1.37 mm), the overall TRE of FLR (2.51 ± 0.93 mm) was lower than that of FBR (5.48 ± 1.81 mm). The results agreed with the intent of the 2 registration techniques: FBR is designed to minimize the FRE, whereas FLR is designed to optimize feature alignment and hence minimize TRE. The overall computational cost of FLR was approximately 4-5 minutes and minimal user interaction was required.

CONCLUSIONS

Because the FLR method directly registers 3DUS with MR by matching internal image features, it proved to be more accurate than FBR in terms of TRE in the 32 patients evaluated in this study. The overall efficiency of FLR in terms of the time and personnel involved is also improved relative to FBR in the operating room, and the method does not require additional image scans immediately prior to surgery. The performance of FLR and these results suggest potential for broad clinical application.

Stereovision to MR image registration for cortical surface displacement mapping to enhance image-guided neurosurgery

PURPOSE

A surface registration method is presented to align intraoperative stereovision (iSV) with preoperative magnetic resonance (pMR) images, which utilizes both geometry and texture information to extract tissue displacements as part of the overall process of compensating for intraoperative brain deformation in order to maintain accurate neuronavigational image guidance during surgery.

METHODS

A sum-of-squared-difference rigid image registration was first executed to detect lateral shift of the cortical surface and was followed by a mutual-information-based block matching method to detect local nonrigid deformation caused by distention or collapse of the cortical surface. Ten (N = 10) surgical cases were evaluated in which an independent point measurement of a dominant cortical surface feature location was recorded with a tracked stylus in each case and compared to its surface-registered counterpart. The full three-dimensional (3D) displacement field was also extracted to drive a biomechanical brain deformation model, the results of which were reconciled with the reconstructed iSV surface as another form of evaluation.

RESULTS

Differences between the tracked stylus coordinates of cortical surface features and their surface-registered locations were 1.94 ± 0.59 mm on average across the ten cases. When the complete displacement map derived from surface registration was utilized, the resulting images generated from mechanical model updates were consistent in terms of both geometry (1-2 mm of model misfit) and texture, and were generated with less than 10 min of computational time. Analysis of the surface-registered 3D displacements indicate that the magnitude of motion ranged from 4.03 to 9.79 mm in the ten patient cases, and the amount of lateral shift was not related statistically to the direction of gravity (p = 0.73 ≫ 0.05) or the craniotomy size (p = 0.48 ≫ 0.05) at the beginning of surgery.

CONCLUSIONS

The iSV-pMR surface registration method utilizes texture and geometry information to extract both global lateral shift and local nonrigid movement of the cortical surface in 3D. The results suggest small differences exist in surface-registered locations when compared to positions measured independently with a coregistered stylus and when the full iSV surface was aligned with model-updated MR. The effectiveness and efficiency of the registration method is also minimally disruptive to surgical workflow.

Estimation of intra-operative brain shift based on constrained Kalman filter

In this study, the problem of estimation of brain shift is addressed by which the accuracy of neuronavigation systems can be improved. To this end, the actual brain shift is considered as a Gaussian random vector with a known mean and an unknown covariance. Then, brain surface imaging is employed together with solutions of linear elastic model and the best estimation is found using constrained Kalman filter (CKF). Moreover, a recursive method (RCKF) is presented, the computational cost of which in the operating room is significantly lower than CKF, because it is not required to compute inverse of any large matrix. Finally, the theory is verified by the simulation results, which show the superiority of the proposed method as compared to one existing method.

Volumetric intraoperative brain deformation compensation: model development and phantom validation

During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.

Serial FEM/XFEM-Based Update of Preoperative Brain Images Using Intraoperative MRI

Current neuronavigation systems cannot adapt to changing intraoperative conditions over time. To overcome this limitation, we present an experimental end-to-end system capable of updating 3D preoperative images in the presence of brain shift and successive resections. The heart of our system is a nonrigid registration technique using a biomechanical model, driven by the deformations of key surfaces tracked in successive intraoperative images. The biomechanical model is deformed using FEM or XFEM, depending on the type of deformation under consideration, namely, brain shift or resection. We describe the operation of our system on two patient cases, each comprising five intraoperative MR images, and we demonstrate that our approach significantly improves the alignment of nonrigidly registered images.

3D XFEM-based modeling of retraction for preoperative image update

Outcomes for neurosurgery patients can be improved by enhancing intraoperative navigation and guidance. Current navigation systems do not accurately account for intraoperative brain deformation. So far, most studies of brain deformation have focused on brain shift, whereas this paper focuses on the brain deformation due to retraction. The heart of our system is a 3D nonrigid registration technique using a biomechanical model driven by the deformations of key surfaces tracked between two intraoperative images. The key surfaces, e.g., the whole-brain region boundary and the lips of the retraction cut, thus deform due to the combination of gravity and retractor deployment. The tissue discontinuity due to retraction is handled via the eXtended Finite Element Method (XFEM), which has the appealing feature of being able to handle arbitrarily shaped discontinuity without any remeshing. Our approach is shown to significantly improve the alignment of intraoperative MRI.

Estimation of brain deformation for volumetric image updating in protoporphyrin IX fluorescence-guided resection

INTRODUCTION

Fluorescence-guided resection (FGR) of brain tumors is an intuitive, practical and emerging technology for visually delineating neoplastic tissue exposed intraoperatively. Image guidance is the standard technique for producing 3-dimensional spatially coregistered information for surgical decision making. Both technologies together are synergistic: the former detects surface fluorescence as a biomarker of the current surgical margin while the latter shows coregistered volumetric neuroanatomy but can be degraded by intraoperative brain shift. We present the implementation of deformation modeling for brain shift compensation in protoporphyrin IX FGR, integrating these two sources of information for maximum surgical benefit.

METHODS

Two patients underwent FGR coregistered with conventional image guidance. Histopathological analysis, intraoperative fluorescence and image space coordinates were recorded for biopsy specimens acquired during surgery. A biomechanical brain deformation model driven by intraoperative ultrasound data was used to generate updated MR images.

RESULTS

Combined use of fluorescence signatures and updated MR image information showed substantially improved accuracy compared to fluorescence or the original (i.e., nonupdated) MR images, detecting only true positives and true negatives, and no instances of false positives or false negatives.

CONCLUSION

Implementation of brain deformation modeling in FGR shows promise for increasing the accuracy of neurosurgical guidance in the delineation and resection of brain tumors.

Brain-skull contact boundary conditions in an inverse computational deformation model

Biomechanical models simulating brain motion under loading and boundary conditions in the operating room (OR) are gaining attention as alternatives for brain shift compensation during open cranial neurosurgeries. Although the significance of brain-skull boundary conditions (BCs) in these models has been explored in dynamic simulations, it has not been fully investigated in models representing the quasi-static brain motion that prevails during neurosurgery. In this study, we extend the application of a brain-skull contact BC by incorporating it into an inversion estimation scheme for the deformation field using the steepest gradient descent (SGD) framework. The technique allows parenchymal surface motion normal to the skull while maintaining stress-free BCs at the craniotomy and minimizing the effect of measurement noise. Application of the algorithm in five clinical cases using sparse data generated at the tumor boundary confirms the significance of brain-skull BCs in the model response. Specifically, the results demonstrate that the contact BC enhances model flexibility and achieves improved or comparable performance at the tumor boundary (recovering about 85% of the deformation) relative to that obtained when normal motion of the parenchymal surface is not allowed. It also significantly improves model estimation accuracy at the craniotomy (1.6mm on average), especially when the normal motion is large. The importance of the method is that model performance significantly improves when brain-skull contact influences the deformation field but does not degrade when the contact is less critical and simpler BCs would suffice. The computational cost of the technique is currently 3.9 min on average, but may be further reduced by applying an iterative solver to the linear systems of equations involved and/or by local refinement of the mesh in regions of interest.