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Compressive Sensing for Cancer Imaging

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  • UserIain Styles (Computer Science)
  • ClockFriday 29 November 2013, 14:00-15:00
  • HousePhysics East 217.

If you have a question about this talk, please contact Vincent Boyer.

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The reconstruction of a signal from a set of discrete samples is normally governed by the Nyquist-Shannon sampling theorem. However under certain conditions, it is possible to reconstruct a signal using a far lower sampling frequency than the Nyquist-Shannon condition specifies. The key feature of the signal that can be exploited in order to break the Nyquist limit is that it must be sparse in some representation, in which case it can be reconstructed using a number of samples that is of the order of non-zero components in the sparse representation. This allows high-resolution signals (images, in our case) to be reconstructed from low-resolution samples. By a happy coincidence, many types of images that are interesting to us can be represented sparsely, and hence compressive sensing methods can be applied. I will review the concepts of compressive sensing, and show a simple practical example of how they can be applied to construct a “single pixel camera” (and discuss why you would want to do this). I will then describe some of our recent work on applying the principles of compressive sensing to the reconstruction of bioluminescence tomography images that are a potentially powerful tool in preclinical cancer studies.

This talk is part of the Cold Atoms series.

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