Gauss Prize Lecture: Compressed sensing — from blackboard to bedside — David Donoho — ICM2018

Compressed sensing — from blackboard to bedside
David Donoho

Abstract: In 2017, next-generation Magnetic Resonance Imaging (MRI) devices by General Electric and Siemens received US Food and Drug Administration approval, allowing them to be used in the US Health care marketplace. This year, Philips has joined them, so that now all three major MRI Scanners have next-generation medical scanners on the market.
The manufacturers of these devices say they are using “Compressed Sensing” (CS) and advertise speedups of 8X over traditional MRI, namely the patient needs to be immobilized for a small fraction of the time previously – a claim which stands up to regulatory scrutiny. The manufacturers say they will eventually spread the use of CS throughout all MRI applications, with a potential scope of 80 million MRI scans per year globally.
A key role in spurring this development was played by articles CS appearing in mathematics journals starting in 2006, for example those by Emmanuel Candes and Terence Tao. It really does seem that mathematical research inspired the MRI industry; in fact the device manufacturers all advertise their devices as using compressed sensing. Still, the transition from mathematics journals to products on the marketplace seems awfully quick. How did this come to be?
The talk will review some of the mathematical ideas preparing the way for the advent of compressed sensing, and some of the ideas which have developed in the aftermath of CS. It will also mention some of the practical work in MRI before and after compressed sensing appeared. It turns out that a very solid preparation both in MRI research and in Mathematics made it possible for such a rapid transition from blackboard to bedside. Also some very inspired and creative MRI researchers, for example Michael Lustig and Shreyas Vasanawala, brought unique talents and energies to bear on this transition.
The talk will cover some of the same territory as the presenter’s recent article in the January 2018 issue of the “Notices of the American Mathematical Society.”

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