Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography

Abstract

Purpose The aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers. Methods Ten patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-Tesla magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-regions-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts. Results Single-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. The two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography (p < 0.001). Conclusions Two-tensor UKF tractography tracks the CST better than single-tensor streamline tractography in the setting of peritumoral edema and crossing fibers in brain tumor patients.

SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

“…Recent applications of SlicerDMRI in neurosurgery and oncology include: visual deficits in pituitary macroadenoma[5], preoperative facial nerve localization in vestibular schwannoma[6], tractography in edematous corticospinaltracts [7], and microstructure alterations in Chiari malformation Type I [8]. We maintain a list of publications using 3D Slicer diffusion functionality such as tensor estimation, fiber tractography, fiber selection, and tractography visualization at dmri.slicer.org/citations.…”

Automated white matter fiber tract identification in patients with brain tumors

“…Images from all gradient directions were retained based on visual inspection of several patient datasets with an in-house tool indicating no gradients should be removed. Thus all 30 gradient directions were retained for analysis (Chen et al, 2015, Chen et al, 2016). We used 3D Slicer to obtain baseline images (B0, the b  =  0 image in the DWI volume) and a binary brain mask derived from the DWI images.…”

“…Whole-brain tractography of both datasets was conducted using a two-tensor unscented Kalman filter method (Chen et al, 2015, Chen et al, 2016, Malcolm et al, 2010) as follows. We used default values for UKF seeding and stopping fractional anisotropy (FA) thresholds, where these defaults have previously been empirically determined across multiple datasets (Chen et al, 2015, Chen et al, 2016, Malcolm et al, 2010).…”

“…This is time consuming, difficult to standardize across patients, produces variable results across operators and software packages Bürgel et al (2009), and is complicated by the displacement of patient-specific brain anatomy due to mass effect. Furthermore, it is increasingly accepted (Caverzasi et al, 2015, Chen et al, 2015, Chen et al, 2016, Farquharson et al, 2013, Kuhnt et al, 2013, Mormina et al, 2015, Nimsky, 2014, Qazi et al, 2009) that for improved clinical anatomical accuracy, tractography must move beyond the standard diffusion tensor imaging (DTI) method, which can only represent one fiber at any location and is thus unable to model fiber crossing. Improved multi-fiber tractography methods, however, increase the difficulty of the expert selection procedure, requiring a higher number of regions of interest to restrict the selection.…”

“…We employ two-tensor unscented Kalman filter tractography (Malcolm et al, 2010), a multi-fiber tractography method that we have recently shown to be more sensitive than the clinical standard of single-tensor tractography in the presence of crossing fibers and edema (Chen et al, 2015, Chen et al, 2016). To identify tracts in a relatively robust way, despite displacement and mass effect, we use a strategy of large-scale features: major fiber tracts such as the arcuate fasciculus (AF) and corticospinal tract (CST) are quite large, traversing many image voxels, and have characteristic shapes and relationships to surrounding tracts.…”