Concerns about post-surgical care for patients with breast cancer have demanded the development of better biomechanical breast models. However, this machine learning approach is one which requires large amounts of high-quality magnetic resonance imaging (MRI) training data that is of difficult acquisition and availability. This can be solved using synthetic data, with the caveat that generating high resolution images comes at the price of very high computational constraints. As such, generating lower resolution samples is both more efficient and yields better results, even if not to the standards of health professionals. Therefore, this work aims to validate a joint approach between lower resolution generative models and the proposed super-resolution architecture, titled Shifted Window Image Restoration (SWinIR), which was used to achieve a 4x increase in image size of breast cancer patient MRI samples. Results prove to be promising and to further expand upon the super-resolution state-of-the-art, achieving good maximum peak signal-to-noise ratio of 41.36 and structural similarity index values of 0.962 and thus beating traditional methods and other machine learning architectures.