Deep learning (DL)-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic datasets. We present a DL framework for MRI reconstruction which does not use fully-sampled data. We test the proposed method in two scenarios: retrospectively undersampled cine and prospectively undersampled abdominal DCE. Our unsupervised method can produce faster reconstructions which are non-inferior to compressed sensing. Our novel proposed method can enable accelerated imaging and accurate reconstruction in applications where fully-sampled data is unavailable.