Joint estimation of spherical harmonic coefficients from magnitude diffusion-weighted images with sparsity constraints

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

Abstract

This paper presents a new method to jointly estimate the spherical harmonic coefficients for all the voxels from noisy magnitude diffusion-weighted images acquired in high angular resolution diffusion imaging. The proposed method uses a penalized maximum likelihood estimation formulation that integrates a noncentral χ distribution based noisy data model, a sparsity promoting penalty on the spherical harmonic coefficients and a joint sparse regularization on the diffusion-weighted image series. An efficient algorithm based on majorize-minimize and alternating direction method of multipliers is proposed to solve the resulting optimization problem. The performance of the proposed method has been evaluated using simulated and experimental data, which demonstrate the improvement over conventional methods in terms of estimation accuracy.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages947-950
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Keywords

  • joint sparsity
  • majorize-minimize
  • noncentral χ distribution
  • penalized maximum likelihood estimation
  • sparse regularization
  • spherical harmonic representation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Joint estimation of spherical harmonic coefficients from magnitude diffusion-weighted images with sparsity constraints'. Together they form a unique fingerprint.

  • Cite this

    Lam, F., Zhao, B., & Liang, Z-P. (2015). Joint estimation of spherical harmonic coefficients from magnitude diffusion-weighted images with sparsity constraints. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 947-950). [7164027] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7164027