Explore a 51-minute conference talk on deep learning-based MR image reconstruction and contrast conversion. Delve into advanced techniques for magnetic resonance imaging, including parallel imaging with deep learning and contrast conversion from multiple weighted images. Learn about applications of deep learning in k-space and image space, as well as the use of variational networks, deep cascade networks, and multi-stream CNNs. Discover innovative approaches such as domain transform learning and automated concepts in image reconstruction. Gain insights into parameter mapping, reconstruction frameworks, and their results. This comprehensive presentation by Dosik Hwang from Yonsei University, delivered at the Institute for Pure & Applied Mathematics at UCLA, offers a thorough exploration of cutting-edge developments in MR imaging technology.