hide
Free keywords:
-
Abstract:
Introduction:
Electrical fields passing through the human skull are affected by its low electrical conductivity and structural irregularity. This issue is relevant both for brain stimulation methods (transcranial direct current stimulation) and electroencephalography (EEG). However, usually such effects are not taken into account. Head models are often overly simplified and unrealistic, entailing a potentially severe loss of predictability to which brain areas are being stimulated in non-invasive brain stimulation (Windhoff et al., 2011) and an imprecise source localization in the case of EEG data (Dannhauer et al., 2011; Rullmann et al., 2009).
Our approach to overcome these limitations is the optimization of specific MR sequences, which allow for a clearer delineation of the human head and skull anatomy. We validate the MR images by acquiring high-resolution computed tomography (CT) data of the same subjects. On basis of the MR sequences, we reconstructed the skull using image processing and surface based methods.
Methods:
We optimized several MR sequences to aid the reconstruction of the human head anatomy on a 3T Philips Achieva system. Particularly relevant were an ultra-short echo time (UTE) sequence (Robson et al., 2003) and a mDixon sequence (Ma, 2008). The mDixon sequence allows separating between fat and water content and prevents the occurrence of chemical shift artifacts. Among others, it is useful to delineate the cortical bone. The UTE images were mainly employed to distinguish between cortical bone and air-filled cavities. Additionally, we acquired a venogram sequence and also standard T1 and T2-weighted images for comparison. We acquired computed tomography images of the head region of the same subjects, using a Siemens Biograph scanner (115mAs, 80keV, 0.6mm slices).
We applied a N3 bias field removal to all MR images (Sled et al., 1998) and coregistered all MR (FSL flirt (Jenkinson et al., 2012)). Combining the T2 image with the venogramm allowed us to apply binary morphology methods (Dogdas et al., 2005) to obtain an inner outline of the skull, including the subdural space. Shrinking and smoothing the inner outline generated a starting volume for the surface growing algorithm, which was guaranteed to not intersect with the skull.
The (energy-based) surface growing algorithm used a constraint image, which we computed as the weighted addition of the mDixon in-phase and fat images, the venogram and air cavities derived from the UTE image. Only if a surface node resulted in a net decrease of its total energy, the surface was expanded at this node. We defined the (node-wise) energy as the sum of its potential energy (provided by the constraint image), its elastic energy (provided by an elasticity term motivated by magnetic spin-lattices) and lastly a repulsion term from other surfaces (in analogy to electrostatic repulsion). Furthermore we introduced node-wise convergence criterions.
Results:
We depict the results of the optimized MR sequences and the computed tomography scan of the same subject in Figure 1. The delineations of the compact and spongy bone are particularly well visible in the mDixon in-phase image. In Figure 2 we display the combination of all MR modalities for the potential energy term (the constraint image), furthermore we show the computed tomography scan. Lastly we present the results of the three-dimensional surface reconstruction algorithm of the inner skull in Figure 3.
Conclusions:
We present and validate optimized MR sequences which can be used for the reconstruction of the human skull. Furthermore we present a surface-growing algorithm for reconstructing the surfaces of the cortical bone in the human skull.
Our work can be readily incorporated into existing surface-based head models, which find usage in both non-invasive brain stimulation simulations and source reconstruction of EEG data. Future work will focus on reducing the number of MR modalities that are needed to achieve accurate results for the skull segmentations.
