Fast Brain MRI Reconstruction Using Deep Iterative Convolutional Neural Network

Abstract

Human brain MRI is usually multi-slice, and there is data redundancy between adjacent slices. Deep learning has become a powerful tool in the field of undersampled MRI reconstruction. However, the current reconstruction algorithms based on deep learning are mainly for a single MRI image. In order to make full use of the data redun-dancy in brain MRI data and obtain higher reconstruction quality and acceleration factor, a deep iterative convolu-tional neural network (DICNN) is proposed. In each iteration, a bi-directional convolution module (BDC) is used to explore the data redundancy between adjacent slices, and then a 2D convolution module (refine net, RNET) is used to further explore the data redundancy within a single MRI slice. Simulation experiments on a single-coil brain MRI dataset show that the proposed algorithm is better than the algorithm based on a single MRI image under different undersampling factors. This method can not only effectively make use of the data redundancy between brain MRI slices and recover more tissue structure details, but also meet real-time MRI reconstruction at a speed of 49 slices per second.

Keywords

• brain magnetic resonance imaging (mri)
• deep learning
• image reconstruction
• convolutional neural network (cnn)