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arXiv · 1807.03191

Approximate k-space models and Deep Learning for fast photoacoustic reconstruction

Andreas Hauptmann·Ben Cox·Felix Lucka·Nam Huynh·Marta Betcke·Paul Beard·Simon Arridge
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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.

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Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard, Simon Arridge. 2018-07-09. Approximate k-space models and Deep Learning for fast photoacoustic reconstruction. https://doi.org/10.1007/978-3-030-00129-2_12

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