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Approximate k-space models and Deep Learning for fast photoacoustic reconstruction (1807.03191v1)
Published 9 Jul 2018 in cs.CV, cs.LG, cs.SD, eess.AS, and math.OC
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.