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Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demandand scan time directly depends on the number of acquired k-space samples.Recently, the deep learning-based MRI reconstruction techniques were suggestedto accelerate MR image acquisition. The most common issues in any deeplearning-based MRI reconstruction approaches are generalizability andtransferability. For different MRI scanner configurations using theseapproaches, the network must be trained from scratch every time with newtraining dataset, acquired under new configurations, to be able to provide goodreconstruction performance. Here, we propose a new generalized parallel imagingmethod based on deep neural networks called NLDpMRI to reduce any structuredaliasing ambiguities related to the different k-space undersampling patternsfor accelerated data acquisition. Two loss functions including non-regularizedand regularized are proposed for parallel MRI reconstruction using deep networkoptimization and we reconstruct MR images by optimizing the proposed lossfunctions over the network parameters. Unlike any deep learning-based MRIreconstruction approaches, our method doesn't include any training step thatthe network learns from a large number of training samples and it only needsthe single undersampled multi-coil k-space data for reconstruction. Also, theproposed method can handle k-space data with different undersampling patterns,and the different number of coils. Experimental results show that the proposedmethod outperforms the current state-of-the-art GRAPPA method and the deeplearning-based variational network method.

Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)