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FASTMRI, Facebook and the Future of Imaging

5:00 pm to 6:00 pm, 12 November 2019

Daniel Sodickson colloquium

Part of UCL Institute in Healthcare Engineering Colloquium series, in collaboration with Centre for Medical Image Computing

Event Information

Open to

All

Availability

Yes

Organiser

UCL Institute of Healthcare Engineering

Location

106 LT Roberts Engineering Building
Gower Street
London
WC1E 6BT
United Kingdom

ABSTRACT

The face of biomedical imaging is changing. This much is clear from a quick inspection of the imaging literature, or of imaging conference agendas, or, increasingly, of the popular press. In the modern radiological landscape, as in the broader landscape of healthcare, disruptive forces and disruptive innovation abound.

One disruptor receiving increasingly far-reaching attention, both in professional circles and in the public eye, is Artificial Intelligence (AI). Taking specific examples from the field of magnetic resonance imaging (MRI), this talk will explore some of the science of AI as applied to the problem of image acquisition and reconstruction, in particular.

Following a quick review of basic principles of image reconstruction with neural networks, I will attempt to highlight differences between purely data-driven learning and what might be called “physics-informed” learning, and will survey early examples of and future possibilities for learning from both static images and time series of imaging data.

I will summarize the goals, working modes, and early outcomes of the recently-announced fastMRI collaboration between NYU School of Medicine and Facebook’s Artificial Intelligence Research team. I will then proceed to trace some noteworthy recent trends in biomedical imaging which may be enabled or enhanced by AI, including a move from traditional snapshots to continuous streaming of imaging data, and a transition from imitating the eye to emulating the brain when it comes to analyzing these data streams.

After assessing the potential impact of such trends on future imaging hardware, I will conclude with a few speculations on how AI and abundant sensor data may affect future modes of perception, both of the interior world of our bodies and of the exterior world that surrounds them.

About the Speaker

Dr Daniel Sodickson

Vice-Chair for Research, Department of Radiology at NYU

Daniel Sodickson headshot
Dr. Sodickson is Vice-Chair for Research in the Department of Radiology at NYU Langone Health, Professor of Radiology and Physiology & Neuroscience at NYU School of Medicine, and Professor of Biomedical Engineering at the NYU Tandon School of Engineering.  He has led a transformation of imaging research at NYU Langone, bringing the Radiology Department’s national research ranking from #17 to #5, and earning the department’s Center for Advanced Imaging Innovation and Research (CAI2R) a designation as a national Biomedical Technology Resource Center.  Dr. Sodickson’s research aims at seeing what has previously been invisible, in order to improve human health. He is credited with founding the field of parallel imaging, in which distributed arrays of detectors are used to gather magnetic resonance images at previously inaccessible speeds. Parallel imaging hardware and software is now an integral part of MRI machines, and is used routinely in MRI scans worldwide. In 2006, Dr. Sodickson was awarded the Gold Medal of the International Society for Magnetic Resonance in Medicine (ISMRM), and he recently completed a term as ISMRM president.  He is in the process of launching a new institute – Tech4Health – designed to bring emerging technologies such as continuous sensing and artificial intelligence to biomedicine.