Deep learning methods have produced impressive results in accelerated magnetic resonance imaging (MRI) reconstruction from under-sampled k-space acquisitions. However, existing MRI reconstruction models are commonly trained with loss functions that uniformly weigh contributions from separate voxels across the field-of-view (FOV), without attributing focus on relatively important regions within the FOV. Furthermore common frameworks for model training rely on availability of large sets of fully-sampled MRI data to construct a ground-truth for the network output. This heavy reliance is undesirable as it is challenging to collect such large datasets in many applications, and even impossible for high spatiotemporal-resolution protocols. In this thesis, we first introduce a self-supervised learning methodology for dynamic cardiac MRI that trains the network to reconstruct acquisitions in the absence of fully-sampled data. We then introduce a segmentation-aware reconstruction framework which implicitly guides the reconstruction process around an ROI with the segmentation error signal. Lastly, we introduce RATNet, a reconstruction framework augmented with attention capabilities which explicitly carries spatial information into the reconstruction process to focus around regions of interest. Self-supervision reduces the excessive demand on fully-sampled data whereas the segmentation-aware re-construction framework backpropagates the spatial information signal in to the reconstruction network. Lastly, RATNet incorporates the attention layers into reconstruction which are sensitive to focusing information supplied by the spatial information network. We demonstrate recovering fully-sampled images from under-sampled acquisitions in cardiac MRI and show their state-of-the-art performance in medical image reconstruction.