title: X-ray computed tomography using curvelet sparse regularization.
abstract: OBJECTIVE
  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.
authors.affiliation: Chair for Computer Aided Medical Procedures (CAMP), Technische Universität München, 85748, Garching, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Wieczorek
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Frikel
authors.affiliation: Chair for Computer Aided Medical Procedures (CAMP), Technische Universität München, 85748, Garching, Germany.
authors.forename: Jakob
authors.initials: J
authors.lastname: Vogel
authors.affiliation: Lehrstuhl für Biomedizinische Physik, Physik-Department and Institut für Medizintechnik, Technische Universität München, 85748, Garching, Germany.
authors.forename: Elena
authors.initials: E
authors.lastname: Eggl
authors.affiliation: Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technische Universität München, 81675, München, Germany.
authors.forename: Felix
authors.initials: F
authors.lastname: Kopp
authors.affiliation: Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technische Universität München, 81675, München, Germany.
authors.forename: Peter B
authors.initials: PB
authors.lastname: Noël
authors.affiliation: Lehrstuhl für Biomedizinische Physik, Physik-Department and Institut für Medizintechnik, Technische Universität München, 85748, Garching, Germany.
authors.forename: Franz
authors.initials: F
authors.lastname: Pfeiffer
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
authors.forename: Laurent
authors.initials: L
authors.lastname: Demaret
authors.affiliation: Chair for Computer Aided Medical Procedures (CAMP), Technische Universität München, 85748, Garching, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Lasser
mesh_terms: D066264:Datasets as Topic; D005269:Femur / Q000000981:diagnostic imaging; D006801:Humans; D019047:Phantoms, Imaging; D011860:Radiography, Abdominal / Q000295:instrumentation / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*