Brain atlases and templates are at the heart of neuroimaging analyses, for which they fa-cilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to over-come this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Graft-ing (VBG), which is a fully-automated, open-source workflow to reliably parcellate MR images in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect.
The core of the VBG approach is the generation of a lesion-free T1-weighted input im-age which enables further image processing operations that would otherwise fail. Here we vali-dated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n=100) derived from healthy control data and patient data.
We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the syn-thetic patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labelling accuracy.
VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations, for example by providing input data for automated solu-tions for fiber tractography or resting-state fMRI analyses that could also be used in the clinic. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG).