Efficiently building receive arrays with electromagnetic simulations and additive manufacturing: A two-layer, 32-channel prototype for 7T brain MRI
Paul-François Gapais, Michel Luong, François Nizery, Gabriel Maitre, Eric Giacomini, Jules Guillot, Alexandre Vignaud, Djamel Berrahou, Marc Dubois, Redha Abdeddaim, Elodie Georget, Sajad Hosseinnezhadian, Alexis Amadon
Abstract
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.