Estimation of fiber orientations using neighborhood information

Chuyang Ye, Jiachen Zhuo, Rao P. Gullapalli, Jerry Ladd Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diffusion magnetic resonance imaging (dMRI) has been used to noninvasively reconstruct fiber tracts. Fiber orientation (FO) estimation is a crucial step in the reconstruction, especially in the case of crossing fibers. In FO estimation, it is important to incorporate spatial coherence of FOs to reduce the effect of noise. In this work, we propose a method of FO estimation using neighborhood information. The diffusion signal is modeled by a fixed tensor basis. The spatial coherence is enforced in weighted ℓ1-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is ensured by the agreement between raw and reconstructed diffusion signals. The resulting objective function is solved using a block coordinate descent algorithm. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for qualitative and quantitative evaluation. The results demonstrate that the proposed method improves the quality of FO estimation.

Original languageEnglish (US)
Title of host publicationComputational Diffusion MRI - MICCAI Workshop, 2015
PublisherSpringer Heidelberg
Pages87-96
Number of pages10
Volumenone
ISBN (Print)9783319285863
DOIs
StatePublished - 2016
EventWorkshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany
Duration: Oct 9 2015Oct 9 2015

Publication series

NameMathematics and Visualization
Volumenone
ISSN (Print)16123786
ISSN (Electronic)2197666X

Other

OtherWorkshop on Computational Diffusion MRI, MICCAI 2015
CountryGermany
CityMunich
Period10/9/1510/9/15

Fingerprint

Fiber Orientation
Fiber reinforced materials
Magnetic Resonance Imaging
Magnetic resonance
Imaging techniques
Fiber
Coordinate Descent
Descent Algorithm
Fibers
Quantitative Evaluation
Voxel
Phantom
Fidelity
Tensors
Brain
Regularization
Tensor
Objective function
Norm
Term

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Geometry and Topology
  • Modeling and Simulation

Cite this

Ye, C., Zhuo, J., Gullapalli, R. P., & Prince, J. L. (2016). Estimation of fiber orientations using neighborhood information. In Computational Diffusion MRI - MICCAI Workshop, 2015 (Vol. none, pp. 87-96). (Mathematics and Visualization; Vol. none). Springer Heidelberg. https://doi.org/10.1007/978-3-319-28588-7_8

Estimation of fiber orientations using neighborhood information. / Ye, Chuyang; Zhuo, Jiachen; Gullapalli, Rao P.; Prince, Jerry Ladd.

Computational Diffusion MRI - MICCAI Workshop, 2015. Vol. none Springer Heidelberg, 2016. p. 87-96 (Mathematics and Visualization; Vol. none).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ye, C, Zhuo, J, Gullapalli, RP & Prince, JL 2016, Estimation of fiber orientations using neighborhood information. in Computational Diffusion MRI - MICCAI Workshop, 2015. vol. none, Mathematics and Visualization, vol. none, Springer Heidelberg, pp. 87-96, Workshop on Computational Diffusion MRI, MICCAI 2015, Munich, Germany, 10/9/15. https://doi.org/10.1007/978-3-319-28588-7_8
Ye C, Zhuo J, Gullapalli RP, Prince JL. Estimation of fiber orientations using neighborhood information. In Computational Diffusion MRI - MICCAI Workshop, 2015. Vol. none. Springer Heidelberg. 2016. p. 87-96. (Mathematics and Visualization). https://doi.org/10.1007/978-3-319-28588-7_8
Ye, Chuyang ; Zhuo, Jiachen ; Gullapalli, Rao P. ; Prince, Jerry Ladd. / Estimation of fiber orientations using neighborhood information. Computational Diffusion MRI - MICCAI Workshop, 2015. Vol. none Springer Heidelberg, 2016. pp. 87-96 (Mathematics and Visualization).
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