Intraoperative Brain Shift Compensation: Accounting for Dural Septa

Abstract

Biomechanical models that describe soft tissue deformation provide a relatively inexpensive way to correct registration errors in image-guided neurosurgical systems caused by nonrigid brain shift. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based methods have been developed recently that allow for uncertainty, yet still capture the first-order effects associated with deformation. The inverse solution is driven by sparse intraoperative surface measurements, which could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies using intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The dural septa act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. A novel method to segment the tentorium cerebelli will be described, along with the procedure for modeling the dural septa. Results in seven clinical cases show a qualitative improvement in subsurface shift accuracy making the predicted deformation more congruous with previous observations in the literature. The results also suggest a considerably more important role for hyperosmotic drug modeling for the intraoperative shift correction environment.

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

“…This is not to say that swelling is not useful in a compensation approach; but rather, thankfully in our work, the incorporation of swelling conditions for use in correction has been found to be quite limited (e.g., in Ref. 13, swelling solutions only influenced about 10% of reconstructed deformations). Nevertheless, the results in Table 7 are promising.…”

“…These approaches use data from intraoperative microscopes, 8,9 laser range scanners, 10,11 and conoscopic holography devices 12 in conjunction with advanced modelbased image-to-physical registration approaches to compensate for deformations. Furthermore, while intraoperative imaging modalities, such as iMR and iCT, are quite compelling, workflow friendly, and inexpensive solutions using sparse data are certainly attractive; and there have been a variety of approaches 11,[13][14][15] in this direction. We should note that while conventional IGNS essentially only requires that preoperative images be available to the neurosurgeon, these more advanced sparse-data-driven image-to-physical approaches can require significantly more preoperative processing.…”

Towards a 3D modeling of brain tumors by using endoneurosonography and neural networks

“…Other studies have shown that the surface of the brain is deformed by up to 20 mm after the skull is opened during neurosurgery, which could lead to substantial error in commercial image-guided surgery systems [31].…”

Anticipation of brain shift in Deep Brain Stimulation automatic planning

“…The law determines the tissue behaviour, but there is no consensus about a constitutive equation unifying all the applications, such as simulating deformation during a car crash or a neurosurgery. Brain deformation models have been widely used for training systems, registration of medical images [10], or compensating brain shift [4], but not for the preoperative planning of a surgery.…”

“…Due to the impossibility to predict CSF loss, [4] precomputed different brain deformations for possible input parameters, but the goal was to update the different structures position during the surgery which is not our case.…”