Revision as of 09:56, 5 August 2015

This page summarizes the existing and the future solutions in RTK for improving image quality of cone-beam (CB) CT images.

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.

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.

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

@@ Line 18: / Line 18: @@
 ** 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 [http://www.openrtk.org/Doxygen/classrtk_1_1WaterPrecorrectionImageFilter.html rtk::WaterPrecorrectionImageFilter]] which implements [http://dx.doi.org/10.1118/1.2188076 <nowiki>[Kachelriess et al, Med Phys, 2006]</nowiki>].
+** Do a tomography of a homogeneous object (e.g., a cylinder) and find the lookup table using the [http://www.openrtk.org/Doxygen/classrtk_1_1WaterPrecorrectionImageFilter.html rtk::WaterPrecorrectionImageFilter] which implements [http://dx.doi.org/10.1118/1.2188076 <nowiki>[Kachelriess et al, Med Phys, 2006]</nowiki>].
 ** Estimate the p and q parameters of Equation 1 in [http://dx.doi.org/10.1007/s003300050710 <nowiki>[Ohnesorge et al, Eur Radiol, 1999]</nowiki>] which comes down to a two-parameters beam-hardening correction and convert it to a lookup table.
 * The algorithm of [http://dx.doi.org/10.1118/1.3477088 <nowiki>[Kyriakou et al, Med Phys, 2010</nowiki>] may easily be implemented from the existing code in RTK.