RTK/ImageQuality: Difference between revisions
Jump to navigation
Jump to search
| Line 25: | Line 25: | ||
* The [http://www.openrtk.org/Doxygen/classrtk_1_1FFTRampImageFilter.html rtk::FFTRampImageFilter] implements the heuristic solution of [http://dx.doi.org/10.1118/1.598855 <nowiki>[Ohnesorge et al, Med Phys, 2000]</nowiki>]. The parameter TruncationCorrection must be adjusted. | * The [http://www.openrtk.org/Doxygen/classrtk_1_1FFTRampImageFilter.html rtk::FFTRampImageFilter] implements the heuristic solution of [http://dx.doi.org/10.1118/1.598855 <nowiki>[Ohnesorge et al, Med Phys, 2000]</nowiki>]. The parameter TruncationCorrection must be adjusted. | ||
* Exact reconstruction based differentiated backprojection and inverse Hilbert filtering (see, e.g., [http://dx.doi.org/10.1088/0031-9155/49/17/006 <nowiki>[Noo et al, PMB, 2004]</nowiki>]) is investigated in Lyon. | * Exact reconstruction based differentiated backprojection and inverse Hilbert filtering (see, e.g., [http://dx.doi.org/10.1088/0031-9155/49/17/006 <nowiki>[Noo et al, PMB, 2004]</nowiki>]) is investigated in Lyon (France). | ||
Revision as of 08:58, 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.
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.
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).