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Comparative Study
. 2016 Feb;75(2):823-30.
doi: 10.1002/mrm.25652. Epub 2015 Mar 7.

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

Yan Wen  1   2 , Yi Wang  2   3 , Tian Liu  1
Affiliations
Comparative Study

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

Yan Wen et al. Magn Reson Med. 2016 Feb.

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.

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

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Figures

Figure 1
Figure 1
A comparison between QSM reconstructions before and after CCD enhancement in a numerical simulation. The first row shows the COSMOS results and the solutions of QSM algorithms. The CCD enhanced results are shown in the second row. The third and fourth rows show the sagittal k-space view of the corresponding results in the first and second row. The arrows indicate the signal variation near the conical surfaces.
Figure 2
Figure 2
A comparison between QSM reconstructions before and after CCD enhancement in a gadolinium phantom. The first row shows the COSMOS results and the solutions of QSM algorithms. The CCD enhanced results are shown in the second row. The third and fourth rows show the sagittal k-space view of the corresponding results in the first and second row. The arrows indicate the signal variation near the conical surfaces.
Figure 3
Figure 3
A comparison between QSM reconstructions before and after CCD enhancement in a human brain scan. The first row shows the COSMOS results and the solutions of QSM algorithms. The CCD enhanced results are shown in the second row. The third and fourth rows show the sagittal k-space view of the corresponding results in the first and second row. The arrows indicate the signal variation near the conical surfaces.
Figure 4
Figure 4
Influence of the parameters on the CCD enhanced brain QSM. A) The change of RMSE and image quality over a range of threshold values. B) The change of RMSE and image quality over a range of alpha values. C) The change of RMSE and image quality over a range of y%.
See this image and copyright information in PMC

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