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Proceedings Paper

Shearlet-based regularization in sparse dynamic tomography
Author(s): T. A. Bubba; M. März; Z. Purisha; M. Lassas; S. Siltanen
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Paper Abstract

Classical tomographic imaging is soundly understood and widely employed in medicine, nondestructive testing and security applications. However, it still offers many challenges when it comes to dynamic tomography. Indeed, in classical tomography, the target is usually assumed to be stationary during the data acquisition, but this is not a realistic model. Moreover, to ensure a lower X-ray radiation dose, only a sparse collection of measurements per time step is assumed to be available. With such a set up, we deal with a sparse data, dynamic tomography problem, which clearly calls for regularization, due to the loss of information in the data and the ongoing motion. In this paper, we propose a 3D variational formulation based on 3D shearlets, where the third dimension accounts for the motion in time, to reconstruct a moving 2D object. Results are presented for real measured data and compared against a 2D static model, in the case of fan-beam geometry. Results are preliminary but show that better reconstructions can be achieved when motion is taken into account.

Paper Details

Date Published: 24 August 2017
PDF: 10 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940Y (24 August 2017); doi: 10.1117/12.2273380
Show Author Affiliations
T. A. Bubba, Univ. of Helsinki (Finland)
M. März, TU Berlin (Germany)
Z. Purisha, Univ. of Helsinki (Finland)
M. Lassas, Univ. of Helsinki (Finland)
S. Siltanen, Univ. of Helsinki (Finland)

Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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