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MR Image Reconstruction as a Computational Imaging Problem: From Model-Based Reconstruction and Sparsity to Machine Learning

October 12 @ 8:00 am - 9:00 am

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 University

Speaker(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

Details

Date:
October 12
Time:
8:00 am - 9:00 am
Event Category:
Website:
https://events.vtools.ieee.org/m/320030

Organizer

[email protected]_edu
Email:
tan@fdu_edu
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