Abstract: Parallel magnetic resonance imaging (PMRI) is an imaging technique that uses multiple receiver coils for undersampling. It utilizes spatial information to supplement the insufficient gradient phase encoding and reconstructs aliasing-free images with specific algorithms to accelerate the imaging process. To address the issue of overfitting or poor generalization when using high acceleration factors with a limited number of auto calibration signals (ACS) in PMRI algorithms based on specific scans, a reconstruction method based on virtual coils and GRAPPA-enhanced networks is proposed. This method expands the sample by using virtual conjugate coils and enhances the ACS using the GRAPPA algorithm for training a nonlinear deep learning network. Experimental results show that the proposed PMRI method can effectively reduce aliasing artifacts caused by insufficient reference data, significantly improving image reconstruction quality with fewer ACS and higher acceleration factors.

Key words: Parallel Magnetic Resonance Imaging, Virtual Coil Concept, Undersampled Images, Deep Learning, Image Reconstruction