IEEE North Jersey Section

In this webinar, two potentially revolutionary energy technologies Power Beaming & Space Solar are discussed.Please click the link for registration for attending the webinar: (https://event.on24.com/wcc/r/3931401/6BD42916E23B11D609280B3339A45703)Co-sponsored by: IEEE North Jersey SectionSpeaker(s): Dr. Paul Jaffe, Agenda: Please click the link for registration for attending the webinar: (https://event.on24.com/wcc/r/3931401/6BD42916E23B11D609280B3339A45703)Virtual: https://events.vtools.ieee.org/m/326974

Over the past two decades, image reconstruction has tremendously gained in importance in MRI enabling reduced scan time, improved image quality, and extracting additional information from the measurements. In this time, 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, compressive 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 the 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 explain the framework of MRI reconstruction as a computational imaging problem and discuss some of the advantages it gives in addressing important clinical needs in MRI.Co-sponsored by: Fairleigh Dickinson UniversitySpeaker(s): Dr. Mariya Doneva, Agenda: Over the past two decades, image reconstruction has tremendously gained in importance in MRI enabling reduced scan time, improved image quality, and extracting additional information from the measurements. In this time, 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, compressive 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 the 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 explain the framework of MRI reconstruction as a computational imaging problem and discuss some of the advantages it gives in addressing important clinical needs in MRI.Room: M105, Bldg: Muscarelle Center, M105, , 1000 River Road , Teaneck , New Jersey, United States, 07666, Virtual: https://events.vtools.ieee.org/m/320030