Magnetic Resonance Imaging (MRI) is a medical imaging modality that offers exquisite resolution and soft-tissue contrast. It is an integral component in diagnostic radiology as well as in basic science research studies due its sensitivity in detecting subtle variations in tissue structure. While MRI can provide a rich source of information, typical acquisition times of 30-40 minutes can limit further widespread use, increase costs, and diminish the patient experience. Moreover, the high-resolution and multi-dimensional MRI datasets can also cause a challenge for efficient and accurate image interpretation. In this talk, through specific examples in musculoskeletal MRI, I will cover recent advances in MRI aided by classical engineering techniques as well as deep learning to substantially reduce the duration of MRI exams and for subsequent image analysis. I will describe how these efforts are helping change the paradigm of MRI by reducing costs and increasing efficiency.