Fast and accurate Magnetic Resonance Image (MRI) reconstruction with NABLA-N network

Reducing Magnetic Resonance (MR) image acquisition time can potentially help to reduce cost and make the MRI based assessment more accessible for the patients care. The GRAPPA and Compressive Sensing (CS) have been applied for image reconstruction from the data where original sampled at a rate inferior to the Nyquist-Shannon sample theory. However, most of the existing techniques are iterative and time-consuming, as a result these methods are not suitable for the real-time applications for MRI reconstruction problem. Nowadays, the Deep Learning (DL) based methods have been showing state-of-the-art performance for different image acquisition and reconstruction tasks including MRI reconstruction. The DL based solution is more robust, generic, transferable, and faster. Hence, the deep learning-based solution can be used to deploy a real-time and faster MRI reconstruction system. In this work, we applied the NABLA-N deep learning model for the MRI reconstruction in both image-domain and frequency-domain (k-space). For evaluating the model, the experiments have conducted on publicly available MRI reconstruction challenge dataset. The results demonstrate that the NABLA-N Net (∇^N-Net) model is capable to reconstruct high-quality MR images and show that kspace reconstruction model is better and more capable compared to image-space based reconstruction model.