Synopsis

The long scan time of the brain MRI limits its applicability in high resolution 3D isotropic imaging. By using the recent Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast (APIR4EMC) method, we propose a high resolution (1 mm) 3D isotropic multi-contrast (T1, T1-Fatsat, T2, PD, FLAIR) brain imaging method with scan time around 10 min on a 3T MR scanner with an 8-channel brain coil. Experimental results demonstrate the effectiveness of this method.

Introduction

Methods

To improve the image quality for multi-contrast imaging from the conventional parallel imaging, APIR4EMC [1] was proposed by encoding the coil sensitivity and also the contrasts correlation into the autocalibration kernel in a GRAPPA-like reconstruction. Based on this, we further propose and validate this method for in-vivo brain imaging with the contrasts T1, T1-Fatsat, T2, PD, and FLAIR. The acquisition was performed on a 3T GE (MR750) scanner with an 8-channel brain coil, using the variable flip angle Fast Spin Echo (FSE) [2] sequence. Common settings: $$$matrix=224\times224\times178$$$, $$$FOV=224\times224\times178mm^3$$$, $$$BW/pixel=41.67kHz$$$, $$$echoes2skip=2$$$ and Radial trajectory [3] with k-space corners skipped. For T1-weighted contrast, $$$TR/TI/ESP/ETL=1062ms/500ms/5.360ms/62$$$. Fat saturation was additionally used for the acquisition of T1-Fatsat contrast. For T2/PD-weighted, $$$TR/ESP/ETL=2000ms/4.688ms/122$$$ with fast recovery. Except for the two skipped echoes, the first and the last 60 of the other echoes are separately placed in PD- and T2- weighted k-spaces, respectively (see Figure 1). For FLAIR, $$$TR/TI/ESP/ETL=5000ms/1700ms/5.360ms/120$$$. A k-space pattern with subsampling factor of $$$2\times3$$$ in two phase encoding (PE) dimensions with a $$$25\times25$$$ calibration region (see Figure 2) is used. The total effective scan time is 9 min.

The autocalibration kernel is learned from the ACS region of all contrasts. Because of the radial trajectory, the ACS region is filled by the first part of each echo train (ET). Differences in ET evolution among different contrasts (see Figure 1) can cause bias in the interpolation of unsampled positions using the kernel estimated from a small part of the ET. To compensate this, the ET of each contrast is stabilized by dividing each echo of each contrast by the norm of the corresponding echo in the additionally acquired zero phase encoding ET. To reduce Gibbs ringing and reduce noise amplification a Fermi filter ($$$kT=0.05$$$, $$$\mu=0.9$$$) is applied in each k-space before reconstruction.

For comparison we reconstruct each k-space separately with GRAPPA, using the same stabilization and Fermi filtering and with the same regularization strength (in kernel estimation) as used by APIR4EMC. The noise amplification map is computed for both APIR4EMC and GRAPPA with 50 pseudo replicas [4].

Results and Discussion

Conclusion

Acknowledgements

References

1. Zhang C, Cristobal-Huerta A, Hernández Tamames J A, et al, APIR4EMC: Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging, Proceeding ISMRM, 2018, 4147.

2. Busse R F, Hariharan H, Vu A, et al. Fast spin echo sequences with very long echo trains: design of variable refocusing flip angle schedules and generation of clinical T2 contrast[J]. Magnetic Resonance in Medicine, 2006, 55(5): 1030-1037.

3. Busse R F, Brau A, Vu A, et al. Effects of refocusing flip angle modulation and view ordering in 3D fast spin echo[J]. Magnetic Resonance in Medicine, 2008, 60(3): 640-649.

4. Robson P M, Grant A K, Madhuranthakam A J, et al. Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions[J]. Magnetic Resonance in Medicine, 2008, 60(4): 895-907.