2D XFEM-based modeling of retraction and successive resections for preoperative image update

Mathematical Modeling and Virtual Reality Simulation of Surgical Tool Interactions With Soft Tissue: A Review and Prospective

This is an informed assessment of the state of the art and an extensive inventory of modeling approaches and methods for soft tissue/medical cutting tool interaction and of the associated medical processes and phenomena. Modeling and simulation through numerical, theoretical, computational, experimental, and other methods was discussed in comprehensive review sections each of which is concluded with a plausible prospective discussion biased toward the development of so-called virtual reality (VR) simulator environments. The finalized prospective section reflects on the future demands in the area of soft tissue cutting modeling and simulation mostly from a conceptual angle with emphasis on VR development requirements including real-time VR simulator response, cost-effective “close-to-reality” VR implementations, and other demands. The review sections that serve as the basis for the suggested prospective needs are categorized based on: (1) Major VR simulator applications including virtual surgery education, training, operation planning, intraoperative simulation, image-guided surgery, etc. and VR simulator types, e.g., generic, patient-specific and surgery-specific and (2) Available numerical, theoretical, and computational methods in terms of robustness, time effectiveness, computational cost, error control, and accuracy of modeling of certain types of virtual surgical interventions and their experimental validation, geared toward ethically driven artificial “phantom” tissue-based approaches. Digital data processing methods used in modeling of various feedback modalities in VR environments are also discussed.

Fabrication of cerebral aneurysm simulator with a desktop 3D printer

Now, more and more patients are suffering cerebral aneurysm. However, long training time limits the rapid growth of cerebrovascular neurosurgeons. Here we developed a novel cerebral aneurysm simulator which can be better represented the dynamic bulging process of cerebral aneurysm The proposed simulator features the integration of a hollow elastic vascular model, a skull model and a brain model, which can be affordably fabricated at the clinic (Fab@Clinic), under $25.00 each with the help of a low-cost desktop 3D printer. Moreover, the clinical blood flow and pulsation pressure similar to the human can be well simulated, which can be used to train the neurosurgical residents how to clip aneurysms more effectively.

In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction

Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).

A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner

BACKGROUND

Brain retraction causes great distortion that limits the accuracy of an image-guided neurosurgery system that uses preoperative images. Therefore, brain retraction correction is an important intraoperative clinical application.

METHODS

We used a linear elastic biomechanical model, which deforms based on the eXtended Finite Element Method (XFEM) within a framework for brain retraction correction. In particular, a laser range scanner was introduced to obtain a surface point cloud of the exposed surgical field including retractors inserted into the brain. A brain retraction surface tracking algorithm converted these point clouds into boundary conditions applied to XFEM modeling that drive brain deformation. To test the framework, we performed a brain phantom experiment involving the retraction of tissue. Pairs of the modified Hausdorff distance between Canny edges extracted from model-updated images, pre-retraction, and post-retraction CT images were compared to evaluate the morphological alignment of our framework. Furthermore, the measured displacements of beads embedded in the brain phantom and the predicted ones were compared to evaluate numerical performance.

RESULTS

The modified Hausdorff distance of 19 pairs of images decreased from 1.10 to 0.76 mm. The forecast error of 23 stainless steel beads in the phantom was between 0 and 1.73 mm (mean 1.19 mm). The correction accuracy varied between 52.8 and 100 % (mean 81.4 %).

CONCLUSIONS

The results demonstrate that the brain retraction compensation can be incorporated intraoperatively into the model-updating process in image-guided neurosurgery systems.

Development of three-dimensional hollow elastic model for cerebral aneurysm clipping simulation enabling rapid and low cost prototyping

OBJECTIVE

We developed a method for fabricating a three-dimensional hollow and elastic aneurysm model useful for surgical simulation and surgical training. In this article, we explain the hollow elastic model prototyping method and report on the effects of applying it to presurgical simulation and surgical training.

METHODS

A three-dimensional printer using acrylonitrile-butadiene-styrene as a modeling material was used to produce a vessel model. The prototype was then coated with liquid silicone. After the silicone had hardened, the acrylonitrile-butadiene-styrene was melted with xylene and removed, leaving an outer layer as a hollow elastic model.

RESULTS

Simulations using the hollow elastic model were performed in 12 patients. In all patients, the clipping proceeded as scheduled. The surgeon's postoperative assessment was favorable in all cases. This method enables easy fabrication at low cost.

CONCLUSION

Simulation using the hollow elastic model is thought to be useful for understanding of three-dimensional aneurysm structure.

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.