Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition.
Neuroimage
; 155: 577-586, 2017 07 15.
Article
em En
| MEDLINE
| ID: mdl-28435102
T2* weighted 3D Gradient Echo (GRE) acquisition is the main sequence used for Susceptibility Weighted Imaging (SWI) and Quantitative Susceptibility Mapping (QSM). These applications require a long echo time (TE) to build up phase contrast, requiring a long repetition time (TR), and leading to excessively lengthy scans. The long TE acquisition creates a significant amount of unused time within each TR, which can be utilized for either multi-echo sampling or additional image encoding with the echo-shift technique. The latter leads to significant saving in acquisition time while retaining the desired phase and T2* contrast. In this work, we introduce the Simultaneous Time Interleaved MultiSlice (STIMS) echo-shift technique, which mitigates slab boundary artifacts by interleaving comb-shaped slice groups with Simultaneous MultiSlice (SMS) excitation. This enjoys the same SNR benefit of 3D signal averaging as previously introduced multi-slab version, where each slab group is sub-resolved with kz phase encoding. Further, we combine SMS echo-shift with Compressed Sensing (CS) Wave acceleration, which enhances Wave-CAIPI acquisition/reconstruction with random undersampling and sparsity prior. STIMS and CS-Wave combination thus yields up to 45-fold acceleration over conventional full encoding, allowing a 15sec full-brain acquisition with 1.5 mm isotropic resolution at long TE of 39 ms at 3T. In addition to utilizing empty sequence time due to long TE, STIMS is a general concept that could exploit gaps due to e.g. inversion modules in magnetization-prepared rapid gradient-echo (MPRAGE) and fluid attenuated inversion recovery (FLAIR) sequences.
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MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Encéfalo
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Imageamento por Ressonância Magnética
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Interpretação de Imagem Assistida por Computador
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Neuroimagem
Limite:
Adult
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Humans
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Male
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article