Model-Updated Image Guidance: A Statistical Approach to Gravity-Induced Brain Shift

Abstract

Abstract. Compensating for intraoperative brain shift using computational models has been used with promising results. Since computational time is an important factor during neurosurgery, a prior knowledge of a patient's orientation and changes in tissue buoyancy force would be valuable information to aid in predicting shift due to gravitational forces. Since the latter is difficult to quantify intraoperatively, a statistical model for predicting intraoperative brain deformations due to gravity is reported. This statistical model builds on a computational model developed earlier. For a given set of patient's orientation and amount of CSF drainage, the intraoperative brain shift is calculated using the computational model. These displacements are then validated against measured displacements to predict the intraoperative brain shift. Though initial results are promising, further study is needed before the statistical model can be used for model-updated image-guided surgery.

An atlas-based method to compensate for brain shift: Preliminary results

“…While the work presented here is encouraging, the following issues need to be addressed before implementing this approach in a MUIGNS system: (i) more detailed validations with intraoperative imaging modalities such that the accuracy of the technique in predicting full volume displacements can be achieved; though validating the accuracy of the model has been reserved for a future study, the phantom results shown here and the simulation study results reported in Dumpuri et al (2003) suggest that the model will behave in a similar fashion when predicting full volume displacement fields from sparse intraoperative data; (ii) sensitivity analysis of the inverse model to the particular selection of the boundary conditions and the consistency of the atlas; (iii) more detailed understanding of the internal structures affecting brain shift, e.g. the falx cerebri has been shown to inhibit crosshemisphere movement; (iv) new studies focused on the improvement from subsurface data such as from co-registered ultrasound; and, (v) more studies regarding the sensitivity of the methods to the number and spatial distribution of sparse intraoperative data points.…”

“…Initially presented in Dumpuri et al (2003), the method reported here extends the earlier framework by incorporating a smoothing constraint to improve the efficiency and accuracy of solution. In order to account for the degree of uncertainty associated with all the sources of deformation, the computational model is run multiple times and these multiple model solutions are combined with the help of a inverse model to predict the intraoperative brain shift.…”

Automated brain shift correction using a pre-computed deformation atlas

“…To increase the accuracy, simulations suggest that dense intraoperative cortical shift measurements may be appropriate. 6,12 In related work, efforts are underway to automate the shift tracking process using LRS such that dense shift measurement points of the cortical surface will be available. 8 Despite the admittedly sparse data field used for constraining the statistical model, these results are encouraging.…”

“…For example, in the biphasic model, the patient's orientation in the OR and the amount of intraoperative CSF drainage are two * The authors found that a threshold value between -0.2 and -0.3 worked best for all patient orientations factors governing the effect of gravitational forces on the brain. 6,10 Although the preoperative surgical plan can provide an estimate of the patient's orientation a priori, estimates for the degree of change in buoyancy forces acting on the brain are somewhat more elusive.…”

“…6 The work presented here extends the framework developed by Davatzikos et al 7 by incorporating elements of principal component analysis (PCA). In addition, an algorithm was developed for the automated generation of multiple sets of boundary conditions in order to facilitate the pre-computation of a "statistical deformation atlas" of solutions.…”