High Pass SENSE

Feng Huang¹, Yu Li¹, Wei Lin¹, Charlie Saylor¹, Arne Reykowski¹

A method to reduce noise/artifact level in images reconstructed by SENSE through artificial image sparsity is presented. The sparsity of an image can be artificially increased through a high pass filter in k-space. With constraint of sparsity, the image reconstructed by regularized SENSE with high pass filtered data can be efficiently denoised. G-factor map is used to produce the sparsity-regularization map. Experiments show that the proposed method reconstructs images with reduced noise level than conventional SENSE, and 1D net acceleration factor 4 can be achieved with an 8-channel coil.