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- Reconstruction algorithm (4) (entfernen)
Computed tomography reconstructions of projected data usually assume an ideal point-like
focal spot. However, in practice, the actual finite focus size leads to blurred projections,
unavoidably. The elements (voxels) of the reconstruction array are smeared differently as a
function of their individual position relative to source and detector. Neglecting the focal
smearing generates considerably blurred reconstructions. Previous attempts were based on
directly deconvolving blurred projections from a constant kernel, which yields better results
but still exhibits artefacts. Iterative algorithms can be adopted to take into account focal
smearing as a function of position. However, this requires detailed prior knowledge of
quantitative smear functions. The DIRECTT (Direct Iterative Reconstruction of Computed
Tomography Trajectories) algorithm is a promising candidate to meet these requirements. It
has been demonstrated elsewhere that DIRECTT copes with limited data sets such as
limited view and region-of- interest data by tracing single sinusoidal-like trajectories in
Radon space, which are selected from the set of all possible trajectories by appropriate
criteria. Currently, position-dependent smearing is used in the projection part of iterations.
At the example of model reconstructions we demonstrate the gain of spatial resolution by
iterative variable desmearing according to the DIRECTT algorithm in comparison to the
standard filtered back-projection.