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Magn Reson Med ; 91(3): 1254-1267, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-37986237

RESUMO

PURPOSE: We propose a comprehensive workflow to design and build fully customized dense receive arrays for MRI, providing prediction of SNR and g-factor. Combined with additive manufacturing, this method allows an efficient implementation for any arbitrary loop configuration. To demonstrate the methodology, an innovative two-layer, 32-channel receive array is proposed. METHODS: The design workflow is based on numerical simulations using a commercial 3D electromagnetic software associated with circuit model co-simulations to provide the most accurate results in an efficient time. A model to compute the noise covariance matrix from circuit model scattering parameters is proposed. A 32-channel receive array at 7 T is simulated and fabricated with a two-layer design made of non-geometrically decoupled loops. Decoupling between loops is achieved using home-built direct high-impedance preamplifiers. The loops are 3D-printed with a new additive manufacturing technique to speed up integration while preserving the detailed geometry as simulated. The SNR and parallel-imaging performances of the proposed design are compared with a commercial coil, and in vivo images are acquired. RESULTS: The comparison of SNR and g-factors showed a good agreement between simulations and measurements. Experimental values are comparable with the ones measured on the commercial coil. Preliminary in vivo images also ensured the absence of any unexpected artifacts. CONCLUSION: A new design and performance analysis workflow is proposed and tested with a non-conventional 32-channel prototype at 7 T. Additive manufacturing of dense arrays of loops for brain imaging at ultrahigh field is validated for clinical use.


Assuntos
Imageamento por Ressonância Magnética , Neuroimagem , Imagens de Fantasmas , Desenho de Equipamento , Razão Sinal-Ruído , Imageamento por Ressonância Magnética/métodos , Fenômenos Eletromagnéticos , Encéfalo/diagnóstico por imagem
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