Medical Image Segmentation and Meshing for Simulating Brain Surgery

Posted on 8 July 2019 by Jessica James

A common cause requiring invasive brain surgery is fatal pressure from swelling-induced deformation. In these situations, neurosurgeons will try to relieve pressure using decompressive craniectomies, wherein they cut into the skull to allow the brain to ‘bulge out’. This method is typically based on personal experience and contains the risk of leaving the patient with severe disabilities. During decompressive craniectomies, nerve fibers in the brain, known as axons, stretch, and run the risk of shearing, making this procedure a ‘last resort’ for surgeons.

Breakthroughs are being made in this area at the Stevens Institute of Technology, Stanford University, Oxford University, and the University of Exeter. Researchers have developed a workflow employing Simpleware software for medical image processing and generation of a Finite Element (FE) model of the brain to simulate craniectomies under different conditions. The use of these methods gives neurosurgeons insight into extreme tissue kinematics, enabling them to plan the shape and position of the craniectomy.

The brain is very difficult to model as it contains billions of neurons and trillions of synapses. Simpleware software was used to reduce this complexity by segmenting key areas of the brain and skull. MRI data was obtained from a female adult’s head using a 3T scanner (GE), before being processed in Simpleware ScanIP. Relevant regions of interest were identified, for example tissue, cerebellum, skin and skull areas.

The next challenge was to generate a simulation-ready FE model from the complex segmented image data. Simpleware FE solved this problem by using proprietary methods to mesh the image data and export a mesh ready for use in a simulation solver. The image-based mesh was highly detailed, including tissues and features, and used color mapping to help identify individual parts of the anatomy.

Simulating Craniectomies

The FE meshes were imported to SIMULIA™ Abaqus for studying craniectomy models with two different skull openings: a unilateral flap and a frontal flap. Abaqus was used to define material models, boundary conditions, and interaction constraints prior to analyzing different swelling scenarios. The goal of the simulations was to predict the mechanical load on the brain from a decompressive craniectomy, including the maximum volumetric expansion of the white matter tissue and specific hemispheres.

Results & Conclusions

This method looked at the effect of the displacement field, maximal principle strain, and radial and tangential axon stretch. Simulations worked to identify an optimal opening size to control pressure and minimize the risk of axonal damage. Results indicated high stretch levels at the edge of the skull opening, leading to predictions that a larger skull opening reduces and distributes axonal loading in the brain. Other results found that opening up the skull on the same side as the swelling produces better patient outcomes.

Personalized head and brain models represent an effective tool for improving outcomes for decompressive craniectomies. Neurosurgeons benefit from new data to help pre-clinical planning, while being able to simulate different scenarios enables patient-specific planning. Longer-term clinical impacts include a reduction in surgical complications, as well as the need for experimental testing. In future, the workflow could be expanded to any scenario involving brain injury or impact, as well as electromagnetic (EM) therapy.

Read more about this research:

• Weickenmeier, J., Saez, P., Butler, C.A.M., Young, P.G., Goriely, A., Kuhl, E., 2017. Bulging Brains. Journal of Elasticity, 129(1-2), 197-212.
• Weickenmeier, J., Butler, C.A.M., Young, P.G., Goriely, A., Kuhl, E., 2017. The mechanics of decompressive craniectomy: Personalized simulations. Computer Methods in Applied Mechanics and Engineering, 314, 180-195.