Fiber Orientation Estimation Guided by a Deep Network

  • Chuyang YeEmail author
  • Jerry L. Prince
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain’s white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted \(\ell _{1}\)-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.


Diffusion MRI Fiber orientation estimation Deep Network Sparse reconstruction 



This work is supported by NSFC 61601461 and NIH 2R01NS056307.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition & Brainnetome Center, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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