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SUMMARY:Compressed Sensing Applications in Functional Magnetic Resonance I
 maging - Christine Law (Oxford University)
DTSTART:20120926T100000Z
DTEND:20120926T110000Z
UID:TALK40100@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:A simple\, fast\, and flexible 1-norm minimization method will
  be demonstrated to deconvolve haemodynamic response function (HRF) from f
 unctional Magnetic Resonance Imaging (fMRI) measurement.  To measure indiv
 idual HRF\, an extra (time consuming\, task limiting) calibration scan has
  commonly been performed before functional scans.  Our method\, based on H
 RF being sparse in a wavelet domain\, instead deconvolves HRF from fMRI ta
 sk data by convex optimization.\n \nCompressed sensing conventionally mean
 s 1-norm approximation to 0-norm minimization.  Advantages and limitations
  of the 1-norm technique and an alternative method for computing 0-norm so
 lution will be discussed.  When 1-norm method fails\, we show how cardinal
 ity-constraint problems can be solved more reliably by a new method called
  convex iteration.\n \nFinally\, We will present a technique to design a l
 owpass finite impulse response (FIR) filter that minimizes the time domain
  impulse response peak amplitude when given a magnitude frequency response
  constraint (phase is unconstrained).  This method uses convex optimizatio
 n technique instead of combinatorial approach.\n
LOCATION:Engineering Department\, CBL Room BE-438
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