“A New Sampling Theory and Its Applications in Magnetic Resonance Imaging ”
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Abstract: In many applications, processing of continuous-time signals is carried out using discrete-time processing of data obtained by sampling. The Nyquist-Shannon sampling theory remarkably shows that a continuous-time signal can be well represented by discrete-time samples under reasonable constraints. More specifically, Nyquist-Shannon sampling theory tells us that when uniformly sampling a continuous-time signal, we must sample at least two times faster than its bandwidth to avoid aliasing and achieve perfect reconstruction. However, in many applications, physical limitations prevent us from sampling at the Nyquist rate. We are often forced to pre-filter the continuous-time signal to reduce its bandwidth and sample at reduced rates. Unfortunately, this yields a reduced-resolution representation of the signal. In other applications, we sample at very high sampling rates and end up with too many samples. We then compress these samples in order to store or transmit them. An emerging theory known as “compressive sampling” or “compressed sensing,” demonstrates that a very large class of signals can be accurately (or under some conditions exactly) reconstructed from far fewer samples than suggested by the Nyquist-Shannon theory. This remarkable result is expected to have far reaching implications in many fields. In this talk, we provide a brief overview of the compressive sampling theory and illustrate how this theory can be applied to magnetic resonance imaging.

Monday, October 15th, 2007

2:00 pm – 3:00pm

Keating 103
Host:  Urs Utzinger, Ph.D. (626-9281)

Persons with a disability may request a reasonable accommodation by contacting the Disability Resource Center at 621-3268 (V/TTY).  Requests should be made as early as possible to allow time to arrange the accommodation.