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Publication Detail
Approximate k-space models and deep learning for fast photoacoustic reconstruction
  • Publication Type:
    Conference
  • Authors:
    Hauptmann A, Cox B, Lucka F, Huynh N, Betcke M, Beard P, Arridge S
  • Publication date:
    12/09/2018
  • Pagination:
    103, 111
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    11074 LNCS
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
© 2018, Springer Nature Switzerland AG. We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The approximate model introduces aliasing artefacts in the gradient information for the iterative reconstruction, but these artefacts are highly structured and we can train a CNN that can use the approximate information to perform an iterative reconstruction. We show feasibility of the method for human in-vivo measurements in a limited-view geometry. The proposed method is able to produce superior results to total variation reconstructions with a speed-up of 32 times.
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