Comparison of probabilistic and deterministic fiber tracking of cranial nerves

Abstract

OBJECTIVE The depiction of cranial nerves (CNs) using diffusion tensor imaging (DTI) is of great interest in skull base tumor surgery and DTI used with deterministic tracking methods has been reported previously. However, there are still no good methods usable for the elimination of noise from the resulting depictions. The authors have hypothesized that probabilistic tracking could lead to more accurate results, because it more efficiently extracts information from the underlying data. Moreover, the authors have adapted a previously described technique for noise elimination using gradual threshold increases to probabilistic tracking. To evaluate the utility of this new approach, a comparison is provided with this work between the gradual threshold increase method in probabilistic and deterministic tracking of CNs. METHODS Both tracking methods were used to depict CNs II, III, V, and the VII+VIII bundle. Depiction of 240 CNs was attempted with each of the above methods in 30 healthy subjects, which were obtained from 2 public databases: the Kirby repository (KR) and Human Connectome Project (HCP). Elimination of erroneous fibers was attempted by gradually increasing the respective thresholds (fractional anisotropy [FA] and probabilistic index of connectivity [PICo]). The results were compared with predefined ground truth images based on corresponding anatomical scans. Two label overlap measures (false-positive error and Dice similarity coefficient) were used to evaluate the success of both methods in depicting the CN. Moreover, the differences between these parameters obtained from the KR and HCP (with higher angular resolution) databases were evaluated. Additionally, visualization of 10 CNs in 5 clinical cases was attempted with both methods and evaluated by comparing the depictions with intraoperative findings. RESULTS Maximum Dice similarity coefficients were significantly higher with probabilistic tracking (p < 0.001; Wilcoxon signed-rank test). The false-positive error of the last obtained depiction was also significantly lower in probabilistic than in deterministic tracking (p < 0.001). The HCP data yielded significantly better results in terms of the Dice coefficient in probabilistic tracking (p < 0.001, Mann-Whitney U-test) and in deterministic tracking (p = 0.02). The false-positive errors were smaller in HCP data in deterministic tracking (p < 0.001) and showed a strong trend toward significance in probabilistic tracking (p = 0.06). In the clinical cases, the probabilistic method visualized 7 of 10 attempted CNs accurately, compared with 3 correct depictions with deterministic tracking. CONCLUSIONS High angular resolution DTI scans are preferable for the DTI-based depiction of the cranial nerves. Probabilistic tracking with a gradual PICo threshold increase is more effective for this task than the previously described deterministic tracking with a gradual FA threshold increase and might represent a method that is useful for depicting cranial nerves with DTI since it eliminates the erroneous fibers withou...

Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters

“…There is still no consensus regarding the choice of tracking algorithms to be used to generate a structural connectome. Probabilistic tracking suffers from false positives (Zolal et al, ) but robust in tracking through crossing fibers (Behrens, Berg, Jbabdi, Rushworth, & Woolrich, ) while deterministic tractography suffers from its inability to track through crossing fibers (Christidi et al, ) and hence can result in false negatives. The effect of both the tracking algorithms and the choice of the edge weights should be evaluated in future work to understand their impact on the topological WM organization.…”

Preoperative diffusion tensor imaging–fiber tracking for facial nerve identification in vestibular schwannoma: a systematic review on its evolution and current status with a pooled data analysis of surgical concordance rates

“…23 Zolal et al studied preoperative FN and cranial nerve positions in 21 patients with large VSs by using nontensorbased probabilistic tractography as compared to the standard tensor-based deterministic tractography. 30 Numerous studies have reported on the superiority of nontensorbased probabilistic tracking in the depiction of smaller fiber bundles, stating that the advantages of probabilistic FT seem to be attributable to better extraction of information about fiber direction in areas where multiple fiber populations occupy the same voxel, as is the situation in cranial nerves. 5,10,12 The probabilistic tracking showed a connection that correlated to the position of the FN in 81% of cases and to the position of the cochlear nerve in 33% of cases.…”

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

“…In the literature, to our knowledge all related studies of the TGN have applied the expert ROI selection strategy (Behan et al, 2017;David Q. Chen, DeSouza, et al, 2016;David Qixiang Chen et al, 2011;Coskun et al, 2017;Fujiwara et al, 2011;Hung et al, 2017;Kabasawa et al, 2007;Moon et al, 2018;Wei et al, 2016;Xie et al, 2020;M. Yoshino et al, 2016;Zolal et al, 2017); however, practical problems remain. First, identification of the TGN is sensitive to ROI placement (Timothée Jacquesson et al, 2018;Xie et al, 2020), where selection of the best-performing ROIs is a challenge.…”

“…First, identification of the TGN is sensitive to ROI placement (Timothée Jacquesson et al, 2018;Xie et al, 2020), where selection of the best-performing ROIs is a challenge. In related work, ROI placement is variable across studies, where adopted ROIs include cisternal portion (CP, also called prepontine cistern, cisternal segment or midpoint of the cisternal segment), root entry zone (REZ), and/or the Meckel's cave (MC) (Behan et al, 2017;David Q. Chen, DeSouza, et al, 2016;David Qixiang Chen et al, 2011;Coskun et al, 2017;Fujiwara et al, 2011;Kabasawa et al, 2007;Moon et al, 2018;Wei et al, 2016;Zolal et al, 2017). Second, placement of ROIs can be affected, or even fail, because of imaging artifacts and/or noise at the complex skull base environment (containing nerve, bone, air, soft tissue and cerebrospinal fluid) (Xie et al, 2020).…”

“…Chen, DeSouza, et al, 2016;David Qixiang Chen et al, 2011;Coskun et al, 2017;Fujiwara et al, 2011;Hung et al, 2017;Krishna et al, 2016;M. Yoshino et al, 2016;Zolal et al, 2017). Fourth, ROI placement depends critically on the experience of trained experts and hence inter-observer variability is a real and ongoing issue of accurate image interpretation (Hakulinen et al, 2012).…”