Synopsis

Parallel Imaging techniques have not been introduced for Gradient and Spin Echo sequences being a limiting factor for clinical use. Enabling PI for GRASE requires new view-ordering schemes that acquire an autocalibration region while simultaneously mitigating artifacts and obtaining the desired contrast. The purpose of this work is to present new 2D PI accelerated Cartesian view-ordering schemes with either T2 or PD contrast in multi-shot VFA 3D-GRASE, for relevant SAR and scan time reduction compared to 3D-FSE/TSE.

Introduction

Gradient and Spin Echo sequence (GRASE¹) has recently been proposed as an alternative to FSE/TSE for structural MRI in high field MRI (7T), where the specific absorption rate (SAR) limits spatial resolution². GRASE is increasingly used for fMRI³ and Arterial Spin Labelling (ASL)⁴ since higher time and spatial resolution can be combined with less susceptibility artifacts compared to EPI⁵.

Currently, a limiting factor for GRASE is that Parallel Imaging techniques (PI)⁶ have not been developed for GRASE. Enabling PI for GRASE requires new view-ordering schemes that acquire an autocalibration region while simultaneously mitigating artifacts and obtaining the desired contrast. The purpose of this work is to present new 2D PI accelerated Cartesian view-ordering schemes with either T2 or PD contrast in multi-shot VFA 3D-GRASE, for relevant SAR and scan time reduction compared to 3D-FSE/TSE.

Methods

Two view-ordering techniques are combined to enable parallel imaging in 3D-GRASE: SORT⁷, which splits off-resonance and T2 effects in different phase encoding directions, and linear modulation⁸ , which mapping the signal modulation into k-space to include 2D acceleration, achieve different image contrasts and mitigate artifacts.

The algorithm uses any k-space sampling pattern, as list of k-space positions, the Echo train length (ETL) and EPI factor (N) as input. To each k-space position it assigns a train number, echo number along the ETL, and the echo in N. To obtain PD-weighted contrast, the center of the k-space has to be filled at the beginning of the Echo Train (ET). This is accomplished by separating the positions with $$$k_{y} \geq k_{y\_center}$$$ and $$$k_{y}<k_{y\_center}$$$ and sorting on $$$k_{z}$$$ each sublist. Subsequently, each sublist is split in N equally sized parts (to make the size equal, the N sublist is completed with zeros), , where list number defines the echo in N. Each sublist is sorted on $$$k_y$$$ and split in ETL parts, specifying echo number, each sorted on $$$k_z$$$. The position in the final list specifies the train number, except that the $$$k_{y} \geq k_{y\_center}$$$ and $$$k_{y}<k_{y\_center}$$$ lists are interleaved along the acquisition (See Figure 1a). To achieve T2-weighted contrast, the center of k-space should be filled at the middle of the ET. For this, the list of k-space positions is sorted on $$$k_{y}$$$ and split in ETL equally sized parts, specifying echo number, sorted on $$$k_{z}$$$. The position in this list divided by N specifies the train number while the position modulo N specifies the echo in N (See Figure 1b) .

Additionally one reference echo train without playing out slice and phase gradients is acquired for phase and amplitude correction⁹. Echoes were time corrected by shifting the echo peak to the center of the time acquisition window. Phase correction was performed by point by point subtraction of the phase of the Fourier transformed reference echoes from the Fourier transformed echoes in the acquisition. Subsequently, the images are reconstructed with Autocalibrating Reconstruction for Cartesian sampling (ARC)^10,11, with sum of squares channels combination. Finally, a correction for gradient non-linearities was applied to each slice.

Results

Discussion

Conclusion

Acknowledgements

References

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