Compressed sensing regularized calibrationless parallel magnetic resonance imaging via deep learning

Abstract The longer scanning time requirement in Magnetic Resonance Imaging (MRI) can one way be overcome through parallel data acquisition technique. The scanning time in pMRI systems can further be reduced through calibrationless data acquisition process that eliminates the need of extra k-space data. Compressed Sensing (CS) or Compressive Sampling in data acquisition again reduces this scanning time as image reconstruction is done from the undersampled k-space measurements. To this aim, the present work proposes a calibrationless CS regularized pMRI approach. A Stacked Convolutional Denoising Autoencoder (SCDAE) is used for accurate estimation of the coil-wise sensitivity maps. The present work also uses an interferometry modulated Radio Frequency (RF) coil that not only enhances the resolution in the Field of View (FoV), but also reduces the interference on the adjacent FoVs. Extensive simulation results show that the proposed method enables reconstruction of an MR image with size 256 × 256 from 25% measurements in approximately 0.57 s and offers a performance improvement in Peak-Signal-to-Noise Ratio (PSNR) by 2 ± 0.18 dB and Structural SIMilarity (SSIM) by 0.2 ± 0.06 over the state-of-the-art methods.