Brain deformation compensation for deep brain lead placement surgery: a comparison of simulations driven by surface vs deep brain sparse data

Accurate surgical placement of electrodes is essential to successful deep brain stimulation (DBS) for patients with neurodegenerative diseases such as Parkinson’s disease. However, the accuracy of pre-operative images used for surgical planning and guidance is often degraded by brain shift during surgery. To predict such intra-operative target deviation due to brain shift, we have developed a finite-element biomechanical model with the assimilation of intraoperative sparse data to compute a whole brain displacement field that updates preoperative images. Previously, modeling with the incorporation of surface sparse data achieved promising results at deep brain structures. However, access to surface data may be limited during a burr hole-based procedure where the size of exposed cortex is too small to acquire adequate intraoperative imaging data. In this paper, our biomechanical brain model was driven by deep brain sparse data that was extracted from lateral ventricles using a Demon’s algorithm and the simulation result was compared against the one resulted from modeling with surface data. Two patient cases were explored in this study where preoperative CT (preCT) and postoperative CT (postCT) were used for the simulation. In patient case one of large symmetrical brain shift, results show that model driven by deep brain sparse data reduced the target registration error(TRE) of preCT from 3.53 to 1.36 and from 1.79 to 1.17 mm at AC and PC, respectively, whereas results from modeling with surface data produced even lower TREs at 0.58 and 0.69mm correspondingly; However, in patient case two of large asymmetrical brain shift, modeling with deep brain sparse data yielded the lowest TRE of 0.68 from 1.73 mm. Results in this study suggest that both surface and deep brain sparse data are capable of reducing the TRE of preoperative images at deep brain landmarks. The success of modeling with the assimilation of deep brain sparse data alone shows the potential of implementing such method in the OR because sparse data at lateral ventricle can be acquired using ultrasound imaging.