Rapid EPI acquisitions are limited by geometric distortion and blurring due to long readouts. Here we incorporate several recent advances in reconstruction approaches and hardware technology to mitigate some of these errors, and implement a multi-echo multi-inversion EPI-based sequence that can be used to find quantitative proton density, T1, T2, and T2* maps. In addition, high quality clinical contrasts can be generated from these maps including T2-, T2*-, T1-, and FLAIR-weighted images. Our protocol provides high-quality, whole-brain, multi-contrast maps with minimal distortion in scan times of 1-3 minutes.
The proposed sequence uses a multi-echo gradient and spin echo (GESE)5,6 acquisition with five echoes per excitation, achieving two gradient echoes, two mixed gradient-and spin echoes, and one spin echo. The multi-echo acquisition is combined with a fast, multiple inversion recovery sequence7, as demonstrated in Fig.1. A non-selective adiabatic inversion pulse is applied before acquiring all slices, reordering the slices every repetition by permuting the slice order by a shift factor (here we used 5). The sequence was tested on two healthy subjects (Siemens Prisma 3T) after written informed consent. The data are acquired with slice-by-slice dynamic shimming using a 32-channel combined RF and B0 shim array (AC/DC coil)4 to decrease the distortion in the phase encoding direction (Fig.2a). Sequence parameters include: FOV=220×220mm, slice thickness = 4mm, 40 slices, first inversion=31ms. Two protocols were implemented:
1) R=4, single shot, 1.2×1.2mm resolution, PF=6/8, TEs=[14 43 87 116 145]ms, TR=8.2s, 8 TIs (65s of acquisition + 32s reference scans)
2) R=8, 3 shots, 1×1mm resolution, PF=7/8, TEs=[16 44 84 112 140]ms, TR=7.5s, 8 TIs (3min of acquisition + 60s of reference scans)
The multi-shot acquisition was reconstructed using MUSSELS1, which has shown great improvement over SENSE (sliding-window) reconstruction (see Fig.2b). The resulting 40 images per slice from each acquisition were fit jointly voxel-wise using Bloch simulations and dictionary matching to achieve PD, T1, T2, and T2* maps. From these maps, clinical contrast images were generated using typical imaging sequence parameters (TI, TE, TR) for each contrast.
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