Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00129-2_12 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Learned image reconstruction,Photoacoustic tomography,Fast fourier methods,Compressed sensing | Conference | 11074 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
6 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Hauptmann | 1 | 18 | 3.62 |
| Ben T. Cox | 2 | 10 | 0.96 |
| Felix Lucka | 3 | 41 | 5.61 |
| Nam Huynh | 4 | 3 | 0.72 |
| Marta M. Betcke | 5 | 17 | 2.77 |
| Paul C. Beard | 6 | 14 | 2.69 |
| Simon R Arridge | 7 | 532 | 74.17 |