The problem of the enormous amount of scattered radiation in kV CBCT (kilo voltage cone beam computer tomography) is addressed. Scatter causes undesirable streak- and cup-artifacts and results in a quantitative inaccuracy of reconstructed CT numbers, so that an accurate dose calculation might be impossible. Image contrast is also significantly reduced. Therefore we checked whether an appropriate implementation of the fast iterative scatter correction algorithm we have developed for MV (mega voltage) CBCT reduces the scatter contribution in a kV CBCT as well. This scatter correction method is based on a superposition of pre-calculated Monte Carlo generated pencil beam scatter kernels. The algorithm requires only a system calibration by measuring homogeneous slab phantoms with known water-equivalent thicknesses. In this study we compare scatter corrected CBCT images of several phantoms to the fan beam CT images acquired with a reduced cone angle (a slice-thickness of 14 mm in the isocenter) at the same system. Additional measurements at a different CBCT system were made (different energy spectrum and phantom-to-detector distance) and a first order approach of a fast beam hardening correction will be introduced. The observed image quality of the scatter corrected CBCT images is comparable concerning resolution, noise and contrast-to-noise ratio to the images acquired in fan beam geometry. Compared to the CBCT without any corrections the contrast of the contrast-and-resolution phantom with scatter correction and additional beam hardening correction is improved by a factor of about 1.5. The reconstructed attenuation coefficients and the CT numbers of the scatter corrected CBCT images are close to the values of the images acquired in fan beam geometry for the most pronounced tissue types. Only for extreme dense tissue types like cortical bone we see a difference in CT numbers of 5.2%, which can be improved to 4.4% with the additional beam hardening correction. Cupping is reduced from 20% to 4% with scatter correction and 3% with an additional beam hardening correction. After 3 iterations (small phantoms) and 6 to 7 iterations (large phantoms) the algorithm converges. Therefore the algorithm is very fast, that means 1.3 seconds per projection for 3 iterations on a standard PC.
Algorithms Calibration