Deep learning for photoacoustic image reconstruction from incomplete data (Conference Presentation)

Andreas Hauptmann; Ben T. Cox; Felix Lucka; Nam Huynh; Marta Betcke; Jonas Adler; Paul Beard; Simon Arridge

doi:10.1117/12.2507210

4 March 2019 Deep learning for photoacoustic image reconstruction from incomplete data (Conference Presentation)

Andreas Hauptmann, Ben T. Cox, Felix Lucka, Nam Huynh, Marta Betcke, Jonas Adler, Paul Beard, Simon Arridge

Proceedings Volume 10878, Photons Plus Ultrasound: Imaging and Sensing 2019; 108781G (2019) https://doi.org/10.1117/12.2507210
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

There are occasions, perhaps due to hardware constraints, or to speed-up data acquisition, when it is helpful to be able to reconstruct a photoacoustic image from an under-sampled or incomplete data set. Here, we will show how Deep Learning can be used to improve image reconstruction in such cases. Deep Learning is a type of machine learning in which a multi-layered neural network is trained from a set of examples to perform a task. Convolutional Neural Networks (CNNs), a type of deep neural network in which one or more layers perform convolutions, have seen spectacular success in recent years in tasks as diverse as image classification, language processing and game playing. In this work, a series of CNNs were trained to perform the steps of an iterative, gradient-based, image reconstruction algorithm from under-sampled data. This has two advantages: first, the iterative reconstruction is accelerated by learning more efficient updates for each iterate; second, the CNNs effectively learn a prior from the training data set, meaning that it is not necessary to make potentially unrealistic regularising assumptions about the image sparsity or smoothness, for instance. In addition, we show an example in which the CNNs learn to remove artifacts that arise when a slow but accurate acoustic model is replaced by a fast but approximate model. Reconstructions from simulated as well as in vivo data will be shown.

Conference Presentation

Citation Download Citation

Andreas Hauptmann, Ben T. Cox, Felix Lucka, Nam Huynh, Marta Betcke, Jonas Adler, Paul Beard, and Simon Arridge "Deep learning for photoacoustic image reconstruction from incomplete data (Conference Presentation)", Proc. SPIE 10878, Photons Plus Ultrasound: Imaging and Sensing 2019, 108781G (4 March 2019); https://doi.org/10.1117/12.2507210

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Image restoration

Photoacoustic spectroscopy

Data acquisition

Neural networks

Convolution

Convolutional neural networks

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years