KEYWORDS: Brain, Finite element methods, 3D modeling, Tumors, Surgery, Neuroimaging, 3D image processing, Magnetic resonance imaging, Data modeling, Ultrasonography
Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of n=9 tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift (>3 mm, n=13±6 measurements per case), the alignment error due to brain shift reduced from 5.7±2.6 to 2.3±1.1 mm, representing ∼59% correction. These first steps toward validation are promising for model-based strategies.
KEYWORDS: Brain, Magnetic resonance imaging, Computational modeling, Surgery, Modeling and simulation, Tumors, Data modeling, Performance modeling, Medical imaging, Image quality
The quality of brain tumor resection surgery is dependent on the spatial agreement between preoperative image and
intraoperative anatomy. However, brain shift compromises the aforementioned alignment. Currently, the clinical standard
to monitor brain shift is intraoperative magnetic resonance (iMR). While iMR provides better understanding of brain shift,
its cost and encumbrance is a consideration for medical centers. Hence, we are developing a model-based method that can
be a complementary technology to address brain shift in standard resections, with resource-intensive cases as referrals for
iMR facilities. Our strategy constructs a deformation ‘atlas’ containing potential deformation solutions derived from a
biomechanical model that account for variables such as cerebrospinal fluid drainage and mannitol effects. Volumetric
deformation is estimated with an inverse approach that determines the optimal combinatory ‘atlas’ solution fit to best
match measured surface deformation. Accordingly, preoperative image is updated based on the computed deformation
field. This study is the latest development to validate our methodology with iMR. Briefly, preoperative and intraoperative
MR images of 2 patients were acquired. Homologous surface points were selected on preoperative and intraoperative scans
as measurement of surface deformation and used to drive the inverse problem. To assess the model accuracy, subsurface
shift of targets between preoperative and intraoperative states was measured and compared to model prediction.
Considering subsurface shift above 3 mm, the proposed strategy provides an average shift correction of 59% across 2
cases. While further improvements in both the model and ability to validate with iMR are desired, the results reported are
encouraging.
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