Comparison of Diffusion Signal Models for Fiber Tractography in Eloquent Glioma Surgery–Determination of Accuracy Under Awake Craniotomy Conditions

Abstract

Fiber tractography (FT) has become an important noninvasive tool to ensure maximal safe tumor resection in eloquent glioma surgery. Intraoperatively applied FT is still predominantly based on diffusion tensor imaging (DTI). However, reconstruction schemes of high angular resolution diffusion imaging data for high-resolution FT (HRFT) are gaining increasing attention. The aim of this prospective study was to compare the accuracy of sophisticated HRFT models compared with DTI-FT. Ten patients with eloquent gliomas underwent surgery under awake craniotomy conditions. The localization of acquisition points, representing deteriorations during intraoperative electrostimulation (IOM) and neuropsychological mapping, were documented. The offsets of acquisition points to the respective fiber bundle were calculated. Probabilistic Q-ball imaging (QBI) and constrained spherical deconvolution (CSD)-FT were compared with DTI-FT for the major language-associated fiber bundles (superior longitudinal fasciculus [SLF] II-IV, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus/medial longitudinal fasciculus). Among 186 offset values, 46% were located closer than 10 mm to the estimated fiber bundle (CSD, 36%; DTI, 40% and QBI, 60%). Moreover, only 10 offsets were further away than 30 mm (5%). Lowest mean minimum offsets (SLF, 7.7 ± 7.9 mm; inferior fronto-occipital fasciculus, 12.7 ± 8.3 mm; inferior longitudinal fasciculus/medial longitudinal fasciculus, 17.7 ± 6.7 mm) were found for QBI, indicating a significant advantage compared with CSD or DTI (P < 0.001), respectively. No significant differences were found between CSD-FT and DTI-FT offsets (P= 0.105), albeit for the compound SLF exclusively (P < 0.001). Comparing HRFT techniques QBI and CSD with DTI, QBI delivered significantly better results with lowest offsets and good correlation to IOM results. Besides, QBI-FT was feasible for neurosurgical preoperative and intraoperative applications. Our findings suggest that a combined approach of QBI-FT and IOM under awake craniotomy is considerable for best preservation of neurological function in the presented setting. Overall, the implementation of selected HRFT models into neuronavigation systems seems to be a promising tool in glioma surgery.