title: 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 without manual intervention.
authors.affiliation: Department and Outpatient Clinic of Neurosurgery and.
authors.forename: Amir
authors.initials: A
authors.lastname: Zolal
authors.affiliation: Department and Outpatient Clinic of Neurosurgery and.
authors.forename: Stephan B
authors.initials: SB
authors.lastname: Sobottka
authors.affiliation: Department and Outpatient Clinic of Neurosurgery and.
authors.forename: Dino
authors.initials: D
authors.lastname: Podlesek
authors.affiliation: Institute of Neuroradiology, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Germany.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Linn
authors.affiliation: Department and Outpatient Clinic of Neurosurgery and.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Rieger
authors.affiliation: Department and Outpatient Clinic of Neurosurgery and.
authors.forename: Tareq A
authors.initials: TA
authors.lastname: Juratli
authors.affiliation: Department and Outpatient Clinic of Neurosurgery and.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Schackert
authors.affiliation: Institute of Neuroradiology, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Germany.
authors.forename: Hagen H
authors.initials: HH
authors.lastname: Kitzler
mesh_terms: D003391:Cranial Nerves / Q000000981:diagnostic imaging*; D056324:Diffusion Tensor Imaging / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D019635:Neurosurgical Procedures / Q000379:methods; D011336:Probability; D019292:Skull Base Neoplasms / Q000000981:diagnostic imaging / Q000601:surgery
keywords: CISS = constructive interference in steady state; CN = cranial nerve; DTI = diffusion tensor imaging; FA = fractional anisotropy; FLAIR = fluid attenuated inversion recovery; FMRIB = Oxford Centre for Functional MRI of the Brain; FSL = FMRIB software library; HCP = Human Connectome Project; KR = Kirby repository; MPRAGE = magnetization prepared rapid gradient-echo; PICo = probabilistic index of connectivity; RESOLVE = readout segmentation of long variable echo-trains; ROI = region of interest; cranial nerves; diffusion tensor imaging; functional neurosurgery; probabilistic tracking; skull base tumors