Figure 1:

Network reconstruction pipeline. (a) The proposed training strategy involves creation of input and target patches from training data. For input data, undersampled k-space data at each TE is processed using NUFFT followed by coil sensitivity weighted combine and subspace filtering using pre-estimated subspace basis. For target data, the radial k-space data is processed using a model-based CS reconstruction. Patches are extracted from the resulting magnitude images and used for supervised learning. In addition, estimated subspace basis and statistics of training data are used during pre-processing and post-processing. (b) For testing/deployment, undersampled k-space data at each contrast is processed using NUFFT and the coil images are combined using coil sensitivity weighted combination. Note that during the testing phase patch extraction is unnecessary since the convolutional kernels can be applied to images.