Literature DB >> 25752805

Enhancing k-space quantitative susceptibility mapping by enforcing consistency on the cone data (CCD) with structural priors.

Yan Wen1,2, Yi Wang2,3, Tian Liu1.   

Abstract

PURPOSE: The inversion from the magnetic field to the magnetic susceptibility distribution is ill-posed because the dipole kernel, which relates the magnetic susceptibility to the magnetic field, has zeroes at a pair of cone surfaces in the k-space, leading to streaking artifacts on the reconstructed quantitative susceptibility maps (QSM). A method to impose consistency on the cone data (CCD) with structural priors is proposed to improve the solutions of k-space methods.
METHODS: The information in the cone region is recovered by enforcing structural consistency with structural prior, while information in the noncone trust region is enforced to be consistent with the magnetic field measurements in k-space. This CCD method was evaluated by comparing the initial results of existing QSM algorithms to the QSM results after CCD enhancement with respect to the COSMOS results in simulation, phantom, and in vivo human brain.
RESULTS: The proposed method demonstrated suppression of streaking artifacts and the resulting QSM showed better agreement with reference standard QSM compared with other k-space based methods.
CONCLUSION: By enforcing consistency with structural priors in the cone region, the missing data in the cone can be recovered and the streaking artifacts in QSM can be suppressed.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  conjugate gradient algorithm; data fitting; quantitative susceptibility mapping

Mesh:

Year:  2015        PMID: 25752805      PMCID: PMC4561604          DOI: 10.1002/mrm.25652

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  24 in total

1.  A global optimisation method for robust affine registration of brain images.

Authors:  M Jenkinson; S Smith
Journal:  Med Image Anal       Date:  2001-06       Impact factor: 8.545

2.  Susceptibility mapping in the human brain using threshold-based k-space division.

Authors:  Sam Wharton; Andreas Schäfer; Richard Bowtell
Journal:  Magn Reson Med       Date:  2010-05       Impact factor: 4.668

3.  Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI.

Authors:  Tian Liu; Pascal Spincemaille; Ludovic de Rochefort; Bryan Kressler; Yi Wang
Journal:  Magn Reson Med       Date:  2009-01       Impact factor: 4.668

4.  Quantitative MR susceptibility mapping using piece-wise constant regularized inversion of the magnetic field.

Authors:  Ludovic de Rochefort; Ryan Brown; Martin R Prince; Yi Wang
Journal:  Magn Reson Med       Date:  2008-10       Impact factor: 4.668

5.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets.

Authors:  Guang-Hong Chen; Jie Tang; Shuai Leng
Journal:  Med Phys       Date:  2008-02       Impact factor: 4.071

6.  Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging.

Authors:  Ludovic de Rochefort; Tian Liu; Bryan Kressler; Jing Liu; Pascal Spincemaille; Vincent Lebon; Jianlin Wu; Yi Wang
Journal:  Magn Reson Med       Date:  2010-01       Impact factor: 4.668

7.  Susceptibility tensor imaging.

Authors:  Chunlei Liu
Journal:  Magn Reson Med       Date:  2010-06       Impact factor: 4.668

8.  Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data.

Authors:  Karin Shmueli; Jacco A de Zwart; Peter van Gelderen; Tie-Qiang Li; Stephen J Dodd; Jeff H Duyn
Journal:  Magn Reson Med       Date:  2009-12       Impact factor: 4.668

9.  Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps.

Authors:  Bryan Kressler; Ludovic de Rochefort; Tian Liu; Pascal Spincemaille; Quan Jiang; Yi Wang
Journal:  IEEE Trans Med Imaging       Date:  2009-06-05       Impact factor: 10.048

10.  Noise Effects in Various Quantitative Susceptibility Mapping Methods.

Authors:  Shuai Wang; Tian Liu; Weiwei Chen; Pascal Spincemaille; Cynthia Wisnieff; A John Tsiouris; Wenzhen Zhu; Chu Pan; Lingyun Zhao; Yi Wang
Journal:  IEEE Trans Biomed Eng       Date:  2013-06-07       Impact factor: 4.538

View more
  5 in total

1.  Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range.

Authors:  Hongjiang Wei; Russell Dibb; Yan Zhou; Yawen Sun; Jianrong Xu; Nian Wang; Chunlei Liu
Journal:  NMR Biomed       Date:  2015-08-27       Impact factor: 4.044

Review 2.  Quantitative Susceptibility Mapping: Concepts and Applications.

Authors:  J R Reichenbach; F Schweser; B Serres; A Deistung
Journal:  Clin Neuroradiol       Date:  2015-07-22       Impact factor: 3.649

3.  Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase.

Authors:  Li Guo; Yingjie Mei; Jijing Guan; Xiangliang Tan; Yikai Xu; Wufan Chen; Qianjin Feng; Yanqiu Feng
Journal:  PLoS One       Date:  2018-05-08       Impact factor: 3.240

4.  Ex-vivo quantitative susceptibility mapping of human brain hemispheres.

Authors:  Arnold M Evia; Aikaterini Kotrotsou; Ashish A Tamhane; Robert J Dawe; Alifiya Kapasi; Sue E Leurgans; Julie A Schneider; David A Bennett; Konstantinos Arfanakis
Journal:  PLoS One       Date:  2017-12-20       Impact factor: 3.240

5.  Mask-Adapted Background Field Removal for Artifact Reduction in Quantitative Susceptibility Mapping of the Prostate.

Authors:  Sina Straub; Julian Emmerich; Heinz-Peter Schlemmer; Klaus H Maier-Hein; Mark E Ladd; Matthias C Röthke; David Bonekamp; Frederik B Laun
Journal:  Tomography       Date:  2017-06
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.