title: Accurate low-dose iterative CT reconstruction from few projections by Generalized Anisotropic Total Variation minimization for industrial CT.
abstract: BACKGROUND
  Reducing the amount of time for data acquisition and reconstruction in industrial CT decreases the operation time of the X-ray machine and therefore increases the sales. This can be achieved by reducing both, the dose and the pulse length of the CT system and the number of projections for the reconstruction, respectively.
  
  
  OBJECTIVE
  In this paper, a novel generalized Anisotropic Total Variation regularization for under-sampled, low-dose iterative CT reconstruction is discussed and compared to the standard methods, Total Variation, Adaptive weighted Total Variation and Filtered Backprojection.
  
  
  METHODS
  The novel regularization function uses a priori information about the Gradient Magnitude Distribution of the scanned object for the reconstruction. We provide a general parameterization scheme and evaluate the efficiency of our new algorithm for different noise levels and different number of projection views.
  
  
  RESULTS
  When noise is not present, error-free reconstructions are achievable for AwTV and GATV from 40 projections. In cases where noise is simulated, our strategy achieves a Relative Root Mean Square Error that is up to 11 times lower than Total Variation-based and up to 4 times lower than AwTV-based iterative statistical reconstruction (e.g. for a SNR of 223 and 40 projections).
  
  
  CONCLUSIONS
  To obtain the same reconstruction quality as achieved by Total Variation, the projection number and the pulse length, and the acquisition time and the dose respectively can be reduced by a factor of approximately 3.5, when AwTV is used and a factor of approximately 6.7, when our proposed algorithm is used.
authors.forename: Maurice
authors.initials: M
authors.lastname: Debatin
authors.forename: Jürgen
authors.initials: J
authors.lastname: Hesser
mesh_terms: D065638:Absorption, Radiation; D000465:Algorithms; D016880:Anisotropy; D007221:Industry / Q000379:methods*; D008422:Materials Testing / Q000379:methods; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D000069079:Radiation Exposure / Q000032:analysis / Q000517:prevention & control*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Gradient Magnitude Distribution; X-ray tomography; iterative CT reconstruction; low-dose; under-sampling