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SUMMARY:Spectral Computed Tomography - James Nagy (Emory University)
DTSTART:20171030T095000Z
DTEND:20171030T104000Z
UID:TALK94006@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>Co-authors: Martin Andersen (Technical University of Den
 mark)\, Yunyi Hu (Emory University)<br></span><span><br>An active area of 
 interest in tomographic imaging is the goal of quantitative imaging\, wher
 e in addition to producing an image\, information about the material compo
 sition of the object is recovered. In order to obtain material composition
  information\, it is necessary to better model of the image formation (i.e
 .\, forward) problem and/or to collect additional independent measurements
 . In x-ray computed tomography (CT)\, better modeling of the physics can b
 e done by using the more accurate polyenergetic representation of source x
 -ray beams\, which requires solving a challenging nonlinear ill-posed inve
 rse problem. In this talk we explore the mathematical and computational pr
 oblem of polyenergetic CT when it is used in combination with new energy-w
 indowed spectral CT detectors. We formulate this as a regularized nonlinea
 r least squares problem\, which we solve by a Gauss-Newton scheme. Because
  the approximate Hessian system in the Gauss-Newton scheme is very ill-con
 ditioned\, we propose a preconditioner that effectively clusters eigenvalu
 es and\, therefore\, accelerates convergence when the conjugate gradient m
 ethod is used to solve the linear subsystems. Numerical experiments illust
 rate the convergence\, effectiveness\, and significance of the proposed me
 thod.</span>
LOCATION:Seminar Room 1\, Newton Institute
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