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Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction

Authors : Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard, Simon Arridge

Published in: Machine Learning for Medical Image Reconstruction

Publisher: Springer International Publishing

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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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previous chapter A U-Nets Cascade for Sparse View Computed Tomography
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Metadata
Title
Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction
Authors
Andreas Hauptmann
Ben Cox
Felix Lucka
Nam Huynh
Marta Betcke
Paul Beard
Simon Arridge
Copyright Year
2018
Book
Machine Learning for Medical Image Reconstruction

Print ISBN: 978-3-030-00128-5

Electronic ISBN: 978-3-030-00129-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00129-2_12

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