Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography

TL;DR

This work tackles misidentification of cranial nerves in diffusion MRI tractography by introducing an anatomy-guided fiber trajectory distribution (FTD) framework. It defines a tract-based FTD function through a higher-order streamline differential model and enforces anatomical priors by constraining the estimate with $div\Omega = 0$ and a diffusion vector field $v(x,y,z)$. The authors present a CN tractography pipeline using centerline priors and Runge-Kutta integration, demonstrating reduced false positives and improved anatomical congruence on both HCP and MDM data. The approach advances noninvasive cranial nerve mapping with potential clinical impact in diagnosis and treatment planning.

Abstract

Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base leads to ambiguous spatial correspondence between diffusion directions and fiber geometry, and existing diffusion tractography methods of CNs identification are prone to producing erroneous trajectories and missing true positive connections. To overcome the above challenge, we propose a novel CNs identification framework with anatomy-guided fiber trajectory distribution, which incorporates anatomical shape prior knowledge during the process of CNs tracing to build diffusion tensor vector fields. We introduce higher-order streamline differential equations for continuous flow field representations to directly characterize the fiber trajectory distribution of CNs from the tract-based level. The experimental results on the vivo HCP dataset and the clinical MDM dataset demonstrate that the proposed method reduces false-positive fiber production compared to competing methods and produces reconstructed CNs (i.e. CN II, CN III, CN V, and CN VII/VIII) that are judged to better correspond to the known anatomy.

Figures (4)

• Figure 1: Schematic representation of CNs fiber streamlines.
• Figure 2: Visualization of the fiber trajectory of the CN II, CN III, CN V, and CN VII/VIII in HCP #100307 subject, overlaid on T2w images.
• Figure 3: Visual comparison of the CN V fiber trajectory obtained on the HCP dataset using UKF, PTT, FTD, and the proposed method. Red arrows point to false-positive fiber generated in the temporal lobe.
• Figure 4: Visual comparison of the CN II fiber trajectory obtained on the MDM dataset using IFOD1, UKF, PTT, and the proposed method.