Automated facial–vestibulocochlear nerve complex identification based on data‐driven tractography clustering

Abstract

Small size and intricate anatomical environment are the main difficulties facing tractography of the facial-vestibulocochlear nerve complex (FVN), and lead to challenges in fiber orientation distribution (FOD) modeling, fiber tracking, region-of-interest selection, and fiber filtering. Experts need rich experience in anatomy and tractography, as well as substantial labor costs, to identify the FVN.Thus, we present a pipeline to identify the FVN automatically, in what we believe is the first study of the automated identification of the FVN. First, we created an FVN template. Forty high-resolution multishell data were used to perform data-driven fiber clustering based on the multishell multitissue constraint spherical deconvolution FOD model and deterministic tractography. We selected the brainstem and cerebellum (BS-CB) region as the seed region and removed the fibers that reach other brain regions. We then performed spectral fiber clustering twice. The first clustering was to create a BS-CB atlas and separate the fibers that pass through the cerebellopontine angle, and the other one was to extract the FVN. Second, we registered the subject-specific fibers in the space of the FVN template and assigned each fiber to the closest cluster to identify the FVN automatically by spectral embedding. We applied the proposed method to different acquirement sites, including two different healthy datasets and two tumor patient datasets.Experimental results showed that our automatic identification results have ideal colocalization with expert manual identification in terms of spatial overlap and visualization. Importantly, we successfully applied our method to tumor patient data.

Automatic oculomotor nerve identification based on data‐driven fiber clustering

“…Based on the experience of our previous work (Zeng et al, 2021), the OCN is present in multiple fiber clusters, and the integration of these fiber clusters can represent an anatomical subdivision of the complete OCN. We obtain the fixed atlas by subdividing multi‐subject registered brainstem fibers into 200 clusters (clusters are labeled as 1–200).…”

Preoperative diffusion tensor imaging: Fiber‐trajectory‐distribution‐based tractography to identify facial nerve in vestibular schwannoma

“…where N cp was the number of correct predictions and N p was the total number of patients. The percentage of misclassification of the method was reflected by the following equation: (10) where N ip was the number of incorrect predictions. The lower the MR indicator, the better, as this may lead the surgeon to incorrect surgical planning prior to surgery.…”

“…6 Numerous studies have shown that preoperative identification of the FN can improve the rate of nerve preservation and predict postoperative deficits. [7][8][9] Diffusion MRI tractography is a potential tool for preoperatively predicting the path of the FN displaced by VS. 7,[10][11][12][13] Deterministic fiber tracking 11,14 and probabilistic fiber tracking 15,16 are the most common methods of identifying FN. The fiber propagation direction of the deterministic fiber tracking method is uniquely determined.…”

“…Diffusion MRI tractography is a potential tool for preoperatively predicting the path of the FN displaced by VS 7,10–13 . Deterministic fiber tracking 11,14 and probabilistic fiber tracking 15,16 are the most common methods of identifying FN.…”