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- W3100262255 abstract "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." @default.
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- W3100262255 date "2018-01-01" @default.
- W3100262255 modified "2023-10-16" @default.
- W3100262255 title "Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction" @default.
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