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This page summarizes the existing and the future solutions in RTK for improving image quality of cone-beam (CB) CT images. It is based on discussions at the RTK meeting on image quality.

X-ray source imperfections

  • Geometric blurring can be corrected by the scatter glare correction detailed in the detector imperfections section.
  • Exposure fluctuations from projection to projection are common. They can be corrected by rtk::I0EstimationProjectionFilter which automatically estimates a constant I0 (intensity without object) per projection using an histogram analysis. This filter only works if there are pixels in each projections that measure x-rays without object (except maybe a few projections using a revursive least-square (RLS) algorithm). The filter does not have any parameter except the bitshift template value for the reduction of the histogram size. It is implemented for integer pixel types only.
  • Focal spot motion cannot be corrected currently. It would require geometric calibration for each acquired projection using auto calibration.

Detector imperfections

  • Variations of the flat field image and the dark field image is known (due to temperature changes or ghosting, see, e.g., [Siewerdsen and Jaffray, Med Phys, 1999]). Acquisition of these two images before and after the acquisition is the best solution when it is possible. There is no other solution in RTK except for the automatic detection of the constant I0 value (see fluctations of the source exposure).
  • Lag corresponds to a short term temporal effect of the detector (a few projections). The thesis of Starman (2010) gives a good overview of the problem and solutions he proposed. rtk::LagCorrectionImageFilter implements equation 2.1 of his PhD thesis. The a and b parameters must be calibrated for a given system, some RTK users have done this and could share their scripts upon request via the RTK user mailing list. A CUDA version of this filter is being implemented in Louvain-La-Neuve (Belgium). The solution in chapter 3 of Starman's thesis might also be investigated in future works.
  • Scatter glare is the point spread function (PSF) of the detector. The solution described in [Poludniowski et al, PMB, 2011] is implemetend in rtk::ScatterGlareCorrectionImageFilter. The a and b parameters must be calibrated for a given system, some RTK users have done this and could share their scripts upon request via the RTK user mailing list.

Beam hardening


Statistical noise

  • RTK has a fast 2D median filter for projection images for a few kernel dimensiosn, see rtk::MedianImageFilter. A GPU version of the median filter will be developed in Salzburg (Austria).
  • Median filters do not preserve edges (see [Arias-Castro and Donoho, Annals of Statistics, 2009]). A multi-pass median filter is required which might be investigated in Louvain-La-Neuve (Belgium) in the future.
  • The Savitzky–Golay filter is a promising solution that will be investigated in Louvain-La-Neuve (Belgium) in the future. This solution also provides derivatives of the image.

Truncated projection images

Geometric calibration

  • RTK allows the description of the CBCT geometry using all 9 degrees-of-freedom (DOF).
  • The 9 DOF are not accounted for in all filters, e.g., the ramp filter does not account for the in plane rotation.
  • Several users have developed 9 DOF calibration.