Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography

Andreas Hauptmann*, Felix Lucka, Marta Betcke, Nam Huynh, Jonas Adler, Ben Cox, Paul Beard, Sebastien Ourselin, Simon Arridge

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

239 Citations (Scopus)

Abstract

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.

Original languageEnglish
Pages (from-to)1382-1393
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume37
Issue number6
Early online date29 Mar 2018
DOIs
Publication statusPublished - 1 Jun 2018

Keywords

  • convolutional neural networks
  • Deep learning
  • iterative reconstruction
  • photoacoustic tomography

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