IEEE Kharagpur Section

Over the past two decades, the significance of MR image reconstruction has tremendously increased, enabling reduced scan time, improved image quality, and extraction of additional information from the measured data. During this period, MRI has witnessed extensive developments in advanced computational algorithms for image reconstruction, many of which have been fueled by signal processing advances in several areas, including multi-channel sampling, compressed sensing, dictionary learning, low-rank and structured low-rank methods. Recently, also neural networks have been employed for image reconstruction achieving further improvements in scan time and image quality. Most importantly, some of these techniques have found their way in the products of MRI vendors and show significant impact in clinical practice. These developments, together with the advancements in computational hardware have opened a new research field of MRI reconstruction as a computational imaging problem. In this talk, I will discuss the framework of MRI reconstruction as a computational imaging problem and the advantages it provides in enhancing the MR performance thereby addressing important clinical needs. Speaker(s): Dr. Mariya Doneva, Virtual: https://events.vtools.ieee.org/m/422352