IEEE SPS SBC Webinar: Improving MRI speed and image quality through model-based reconstruction (By Dr. Mariya Doneva)
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.
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- Date: 31 May 2024
- Time: 01:30 PM to 02:30 PM
- All times are (UTC+05:30) Chennai
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Speakers
Dr. Mariya Doneva
Improving MRI speed and image quality through model-based reconstruction
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.
Biography:
Dr. Doneva is a Senior Scientist at Philips Innovative Technologies, Hamburg, Germany, which she joined in 2010. She received her BSc and MSc degrees in Physics from the University of Oldenburg in 2006 and 2007, respectively and he PhD degree in Physics from the University of Lübeck in 2010. She was a Research Associate at Electrical Engineering and Computer Sciences Department at UC Berkeley between 2015 and 2016. Her work has yielded many innovations related to imaging workflow improvements, novel quantitative MRI
approaches, and most prominently fast MRI data acquisition based on compressed sensing allowing significant reduction of the scan time of routine clinical scans, which has been already integrated in the clinical routine of many hospitals and used to scan millions of patients. She has been granted over 30 patents for her work in MR imaging.
Dr. Doneva was an Organizing Committee Member of multiple conferences including the International Society for Magnetic Resonance in Medicine (ISMRM) (2019-2021), IEEE International Symposium on Biomedical Imaging (ISBI) (2020), the ISMRM Workshop on Data Sampling and Image Reconstruction (2020), and the SIAM Conference on Imaging Science 2022.
She was Guest Editor, IEEE Signal Processing Magazine Special Issue on Computational MRI: Compressive Sensing and Beyond; Editor, comprehensive reference book on Quantitative Magnetic Resonance Imaging; Editorial Board Member, Magnetic Resonance in Medicine and IEEE Transactions on Computational Imaging; and Editor of a reference book on MR image reconstruction. Dr. Doneva’s research interests include methods for efficient data acquisition, image reconstruction and quantitative parameter mapping in the context of magnetic resonance imaging. Her work involves developing mathematical optimization and
signal processing approaches that aim at improving the MR scan efficiency and obtaining robust and reliable (multi-parametric) quantitative information for diagnostics and therapy follow up.
Address:Hamburg, , , Germany