Anatomy-guided fiber trajectory distribution estimation for cranial
  nerves tractography
Lei Xie Qingrun Zeng Huajun Zhou Guoqiang Xie Mingchu Li Jiahao Huang Jianan Cui Hao Chen Yuanjing Feng
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.