Abstract: An MRI RF coil that is optimized for SENSE imaging is used in a method where the surface current density distribution on a coil former was calculated to maximize the SNRsense within a volume of interest. An analytic relationship was formulated between the SNRsense and surface current density on the coil former. The SNR at pixel ? in a SENSE-MR image, SNRsense,?, is inversely proportional to the g-factor. The g-factor was formulated in terms of the B1 distribution of the coils. By specifying the geometry of the desired coil former and using a finite element mesh, the surface current distribution was calculated to maximize the SNRsense, by minimizing (1/SNRsense) in the volume of interest using a least squares procedure. A simple two-coil array was tested and phantom images were collected. The results show that the new coil design method yielded better uniformity and SNR compared to standard coils.

Abstract: An MRI RF coil that is optimized for SENSE imaging is used in a method where the surface current density distribution on a coil former was calculated to maximize the SNRsense within a volume of interest. An analytic relationship was formulated between the SNRsense and surface current density on the coil former. The SNR at pixel ? in a SENSE-MR image, SNRsense,?, is inversely proportional to the g-factor The g-factor was formulated in terms of the B1 distribution of the coils. By specifying the geometry of the desired coil former and using a finite element mesh, the surface current distribution was calculated to maximize the SNRsense, by minimizing (1/SNRsense) in the volume of interest using a least squares procedure. A simple two-coil array was tested and phantom images were collected. The results show that the new coil design method yielded better uniformity and SNR compared to standard coils.