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

doi:10.1007/978-3-030-00129-2_12

paper

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

arXiv:1807.03191 · doi:10.1007/978-3-030-00129-2_12

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.