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Wieczorek, M.* ; Frikel, J. ; Vogel, J.* ; Eggl, E.* ; Kopp, F.* ; Noel, P.B.* ; Pfeiffer, F.* ; Demaret, L. ; Lasser, T.*

X-ray computed tomography using curvelet sparse regularization.

Med. Phys. 42, 1555-1565 (2015)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Purpose: Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. Methods: In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Results: Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method’s strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. Conclusions: The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.  
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Curvelets ; Sparse Regularization ; X-ray Computed Tomography; Iterative Image-reconstruction; Total-variation Minimization; Proximal Point Algorithm; Linear Inverse Problems; Monotone-operators; Ct Reconstruction; Tensor-framelet; Beam Ct; Transform; Resolution
ISSN (print) / ISBN 0094-2405
e-ISSN 1522-8541
Zeitschrift Medical Physics
Quellenangaben Band: 42, Heft: 4, Seiten: 1555-1565 Artikelnummer: , Supplement: ,
Verlag American Institute of Physics (AIP)
Verlagsort Melville
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)