Non-rigid Deformation Pipeline for Compensation of Superficial Brain Shift

Abstract

The correct visualization of anatomical structures is a critical component of neurosurgical navigation systems, to guide the surgeon to the areas of interest as well as to avoid brain damage. A major challenge for neuronavigation systems is the brain shift, or deformation of the exposed brain in comparison to preoperative Magnetic Resonance (MR) image sets. In this work paper, a non-rigid deformation pipeline is proposed for brain shift compensation of preoperative imaging datasets using superficial blood vessels as landmarks. The input was preoperative and intraoperative 3D image sets of superficial vessel centerlines. The intraoperative vessels (obtained using 3 Near-Infrared cameras) were registered and aligned with preoperative Magnetic Resonance Angiography vessel centerlines using manual interaction for the rigid transformation and, for the non-rigid transformation, the non-rigid point set registration method Coherent Point Drift. The rigid registration transforms the intraoperative points from the camera coordinate system to the preoperative MR coordinate system, and the non-rigid registration deals with local transformations in the MR coordinate system. Finally, the generation of a new deformed volume is achieved with the Thin-Plate Spline (TPS) method using as control points the matches in the MR coordinate system found in the previous step. The method was tested in a rabbit brain exposed via craniotomy, where deformations were produced by a balloon inserted into the brain. There was a good correlation between the real state of the brain and the deformed volume obtained using the pipeline. Maximum displacements were approximately 4.0 mm for the exposed brain alone, and 6.7 mm after balloon inflation.

Superficial vessel reconstruction with a Multiview camera system

“…Once the correspondences are known, a nonrigid transformation can be applied to the preoperative image to compensate for the brain shift. 29 The TPS process has some similarity to the work of Vivanti et al, 7 who used the random sample consensus 56 and a parabolic model fitting to obtain points on the brain surface. The parabolic model has much fewer DOF than the TPS, and for complex curves a low degree model may not be the most adequate to fit the data.…”

“…The main differences are the underlying methods and the purpose. The compensation for brain shift using this data is presented in our work, 29 where we focus on the final stages of the overall brain shift pipeline using superficial blood vessels.…”

Deformation Aware Augmented Reality for Craniotomy Using 3D/2D Non-rigid Registration of Cortical Vessels

“…During a craniotomy, the cortical brain surface is revealed and can be used as an additional source of information. Filipe et al [16] used 3 Near-Infrared cameras to capture brain surface displacement which is then registered to MRI scans using the coherent point drift method by considering vessel centerlines as strong matching features. A new deformed MRI volume is generated using a thin plate spline model.…”

Registration of vascular structures using a hybrid mixture model

“…For example, blood vessel centerlines extracted from preoperative MRIs and intraoperative ultrasound data were aligned by applying the ICP algorithm [27]. In [9,15], CPD was used to compensate for intraoperative brain shift. Both studies used thin plate splines (TPS)-based interpolation to warp the preoperative image to its intraoperative counterparts.…”