RTK/ImageQuality

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/Meetings/OnImageQualityInStaticCBCT|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 weighting constant per projection using an histogram analysis. This filter only works if there are pixels in each projections that measure x-rays that traversed air only (except maybe a few projections using a revursive least-square . 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 acquisition.

Detector imperfections

• Lag: the [ rtk::] filter implements correction implements

, see [Poludniowski et al, PMB, 2011] implemetend in [rtk::]

Beam hardening

• The acquired data may be linearized for a given material using rtk::LookupTableImageFilter as explained, e.g., in Fig. 1 of [Brooks and Di Chiro, PMB, 1976]. There are several solutions to compute the lookup table:
  • Compute it from the knowledge of the spectrum of the x-ray source and the detector response,
  • Measure the attenuation for several thicknesses of the material of interest,
  • Do a tomography of a homogeneous object (e.g., a cylinder) and find the lookup table using the rtk::WaterPrecorrectionImageFilter which implements [Kachelriess et al, Med Phys, 2006].
  • Estimate the p and q parameters of Equation 1 in [Ohnesorge et al, Eur Radiol, 1999] which comes down to a two-parameters beam-hardening correction and convert it to a lookup table.
• The algorithm of [Kyriakou et al, Med Phys, 2010] may easily be implemented from the existing code in RTK.

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

• The rtk::FFTRampImageFilter implements the heuristic solution of [Ohnesorge et al, Med Phys, 2000]. The parameter TruncationCorrection must be adjusted.
• Exact reconstruction based differentiated backprojection and inverse Hilbert filtering (see, e.g., [Noo et al, PMB, 2004]) is investigated in Lyon (France).