Gradient Nonlinearity and B0-induced Distortion Corrections of Prospective Motion Correction Data at 7T MRI
Uten Yarach1, Daniel Stucht1, Hendrik Mattern1, Frank Godenschweger1, and Oliver Speck1

1Department of Biomedical Magnetic Resonance, Otto-von-Guericke University Magdeburg, Magderburg, Germany

Synopsis

Patient motion during an MRI of the brain can result in non-diagnostic image quality. Even with perfect prospective motion (PMC) tracking and correction, the varying coil sensitivity, gradient non-linearity, and B0 field shift can cause significant artifacts that cannot be corrected prospectively. Recently, a model-based MR image reconstruction via iterative solver was employed to minimize the sensitivity misalignment due to physiological movement. In this study, we extended the mentioned model by gradient-warped and B0-induced distortion corrections to reconstruct the PMC MR data. The result shows the improvement that is remarkably reduced artifacts after a few iterations of the proposed technique.

INTRODUCTION

Even with highly accurate prospective motion correction (PMC)1, for large motion, the corrected images can be degraded by residual artifacts. The change in B0 field (∆B0) and gradient nonlinearity (GNL) are important sources of distortion and may impact PMC data. This work proposed an image reconstruction to mitigate the geometric distortion in PMC data by integrating corrections of GNL and ∆B0 into the augmented SENSE reconstruction2.

MATERIAL and METHOD

Data acquisition: Experiments were performed on a 7T MR (Siemens) equipped with a 32-channel head coil. The application of the proposed method to real motion with PMC enabled was performed using 3D MPRAGE with matrix; 256x256x176, voxel size; 1 mm3, TR/TE/TI; 1800/1.99/1050 ms, and BW; 200 Hz/pixel. After the first half of the acquisition, the volunteer was instructed to perform a one-time head rotation around the z-axis. The motion pattern from the tracking log file is shown in figure 1a. The field maps were also measured before and after motion using dual-echo 3D FLASH with the same resolution as 3D MPRAGE, TR/TE1/TE2; 10.00/3.06/5.84 ms, and BW; 250 Hz/pixel.

Image reconstruction: We aimed to obtain the distortion-free PMC MR images () by minimizing the residual sum of squares (Eq. 1) using the Conjugate Gradient (CG) method3,

$$min_v\left\{∑_{i,j}‖m^{i,j}−M^iFG^i_{reg2warp}C^{i,j}v‖_2^2+λ‖Lv‖_2^2\right\} ----(1)$$

where $m^{i,j}$ is MR measurement data at pose $i$ and coil $j$ which is aligned to a Cartesian grid by PMC. Coil sensitivity ($C^{i,j}$) was estimated from the central 64x64x64 k-space data of 3D FLASH (TE1) acquired using a cosine tapper window4, $M^i$ is a Cartesian undersampling mask (1=sampled, 0=otherwise), $F$ is fast Fourier transform. $G^i_{reg2warp}$ is a 3D regular Cartesian to warped coordinate operator transferring the image data from regular to warped coordinate systems. Following the nonuniform FFT (NUFFT) algorithm5, we implemented the operator using a Kaiser-Bessel interpolation with optional 2x oversampling. The warped coordinates corrupted by GNL and ∆B0 at any pose $i$ were prepared prior to solving Eq. 1 using the vendor-provided Spherical Harmonics expansion of the gradient distortions and the dual-echo field map method6, respectively. The regularization parameter $λ$ is a positive real constant that was adjusted manually in this study, and $L$ is here a tri-diagonal (1 -2 1) sparse matrix7.

RESULTS

Figure 1a shows the motion pattern when the subject performed the head rotation during the data acquisition. A maximum rotation around the z-axis of approx. 23 degrees was detected. Figure 1b demonstrates the effectiveness of the proposed method for undersampled k-space data, the full PMC k-space data were artificially accelerated by factor 2x2 along both phase directions (phase and slice directions). Although, the conventional CG-SENSE [4] provided acceptable images (1st column), the remaining aliasing artifacts due to coil sensitivity misalignment and also blurring caused by ∆B0 and GNL are clearly visible as shown in the red circles. Note that sensitivity maps specific to the first half of the acquisition were used in conventional CG-SENSE. Superior image quality with very little remaining artifacts was achieved after applying the augmented CG-SENSE with integrated ∆B0+GNL corrections (2nd column).

DISCUSSIONS

In this study, PMC had a high accuracy in detecting the motion since the residuals of rigid motion errors were not visually identifiable after correcting non-rigid distortion by the proposed technique. We also found that using a tri-diagonal sparse matrix7 in the regularization provided smoother solution than using an identity matrix. It accounted for the high intensity variations in strong field inhomogeneity regions. Using this regularized matrix with an automatic λ selection technique8 may be practical. The requirement for a field map per motion pose is a limitation of the method. Using a magnetic field model9 which neither requires additional scan time nor suffers from low SNR at air/tissue boundaries may be feasible. In conclusion, GNL and ∆B0 can induce residual artifacts even with perfect prospective motion correction. These artifacts can be alleviated by the proposed technique when the relevant field information is available.

Acknowledgements

The study was supported by the BMBF (Forschungscampus STIMULATE, 03FO16101A) and NIH (1R01-DA021146).

References

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Figures

Fig.1. (a) The motion pattern during data acquisition. (b) The 2x2 undersampled k-space data reconstructed by the CG-SENSE and the proposed technique. The axial images in the 2nd and 3rd rows correspond to the white lines #1 and #2, respectively. The sagittal images in the 4th row correspond to the white lines #3.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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