Parallel MRI Reconstruction Using Broad Learning System

As an inverse problem, parallel magnetic resonance imaging (pMRI) reconstruction accelerates imaging speed by interpolating missing k-space data from a group of phased-array coils. Deep learning methods have been used for improving pMRI reconstruction quality in recent years. However, deep learning approaches need a large amount of training data that are acquired from different hardware configurations and anatomical areas. Data distributions may be different between training data and testing data, and a long-time training is needed. In this work, we proposed a broad learning system based parallel MRI reconstruction that exploits approximation capability of one-layer neural network through broadening network structure with expanded nodes. Experimental results show that the proposed method is able to suppress noise in compared to the conventional pMRI reconstruction.