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Magn Reson Med ; 79(1): 264-275, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-28321904

RESUMO

PURPOSE: To accelerate high-resolution diffusion imaging, rotating single-shot acquisition (RoSA) with composite reconstruction is proposed. Acceleration was achieved by acquiring only one rotating single-shot blade per diffusion direction, and high-resolution diffusion-weighted (DW) images were reconstructed by using similarities of neighboring DW images. A parallel imaging technique was implemented in RoSA to further improve the image quality and acquisition speed. RoSA performance was evaluated by simulation and human experiments. METHODS: A brain tensor phantom was developed to determine an optimal blade size and rotation angle by considering similarity in DW images, off-resonance effects, and k-space coverage. With the optimal parameters, RoSA MR pulse sequence and reconstruction algorithm were developed to acquire human brain data. For comparison, multishot echo planar imaging (EPI) and conventional single-shot EPI sequences were performed with matched scan time, resolution, field of view, and diffusion directions. RESULTS: The simulation indicated an optimal blade size of 48 × 256 and a 30 ° rotation angle. For 1 × 1 mm2 in-plane resolution, RoSA was 12 times faster than the multishot acquisition with comparable image quality. With the same acquisition time as SS-EPI, RoSA provided superior image quality and minimum geometric distortion. CONCLUSION: RoSA offers fast, high-quality, high-resolution diffusion images. The composite image reconstruction is model-free and compatible with various diffusion computation approaches including parametric and nonparametric analyses. Magn Reson Med 79:264-275, 2018. © 2017 International Society for Magnetic Resonance in Medicine.


Assuntos
Encéfalo/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética , Interpretação de Imagem Assistida por Computador , Imagens de Fantasmas , Algoritmos , Simulação por Computador , Imagem Ecoplanar , Voluntários Saudáveis , Humanos , Aumento da Imagem/métodos , Processamento de Imagem Assistida por Computador , Reprodutibilidade dos Testes
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