Parallel Magnetic Resonance Imaging using Neural Networks

Magnetic resonance imaging of dynamic events such as cognitive tasks in the brain, requires high spatial and temporal resolution. In order to increase the resolution in both domains simultaneously, parallel imaging schemes have been in existence, where multiple receiver coils are used, each of which needs to acquire only a fraction of the total available signal. In our approach, we regularly un-dersample the signal at each of the receiver coils and the resulting aliased coil images are combined (unaliased) using the neural network framework. Data acquisition follows a variable-density sampling scheme, where lower frequencies are densely sampled, and the remaining signal is sparsely sampled. The low resolution images obtained using the densely sampled low frequencies are used to train the neural network. Reconstruction of the image is carried out by feeding the high-resolution aliased images to the trained network. The proposed approach has been applied to phantom as well as real brain MRI data sets, and results have been compared with the standard existing parallel imaging techniques. The proposed approach is found to perform better than the standard existing techniques.