TY  - GEN
N2  - 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.
EP  - 111
A1  - Hauptmann, A
A1  - Cox, B
A1  - Lucka, F
A1  - Huynh, N
A1  - Betcke, M
A1  - Beard, P
A1  - Arridge, S
UR  - http://dx.doi.org/10.1007/978-3-030-00129-2_12
CY  - Cham, Switzerland
ID  - discovery10058919
PB  - Springer
N1  - © 2018, Springer Nature Switzerland AG. This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
Y1  - 2018/09/12/
TI  - Approximate k-space models and deep learning for fast photoacoustic reconstruction
AV  - public
KW  - Learned image reconstruction · Photoacoustic tomography
· Fast Fourier methods · Compressed sensing
SP  - 103
SN  - 1611-3349
T3  - Lecture Notes in Computer Science
ER  -