Cardiac CT image reconstruction suffers from artifacts due to heart motion during acquisition. In order to mitigate these effects, it is common practice to choose a protocol with minimal gating window and fast gantry rotation. In addition, it is possible to estimate heart motion retrospectively and to incorporate the information
in a motion-compensated reconstruction (MCR). If shape tracking algorithms are used for generation of the heart motion-vector field (MVF), the number and positions of the motion vectors will not coincide with the number and positions of the voxels in the reconstruction grid. In this case, data interpolation is necessary for
MCR algorithms which require one motion vector at each voxel location. This work examines different data interpolation approaches for the MVF interpolation problem and the effects on the MCR results.
In scintillating detectors x-rays are converted to luminescent photons with a time delay. The corresponding time
resolution of the detector can have - in contrast to usual
multi-slice CT - a deteriorating effect in new CT concepts with
multiple sources illuminating one detector, because x-ray intensities measured here in consecutive projections
correspond to the absorption along paths through very different regions of the object. A new analytical description of
these effects is presented and a correction algorithm is derived. It is also shown that the detector time delay and its
correction can lead to a noticeable increase of image noise.
The raw data generated by a computed tomography (CT) machine are not readily usable for reconstruction. This is the result of various system non-idealities, and although the deterministic nature of corruption effects like crosstalk and afterglow permits removal through deconvolution, there is the drawback that deconvolution increases noise.
Methods that perform raw data correction combined with noise suppression are commonly termed sinogram restoration methods. The need for sinogram restoration arises, for example, when photon counts are low and non-statistical reconstruction algorithms like filtered backprojection are used.
Many modern CT machines offer a so-called dual focal spot (DFS) mode, which serves the goal of increased radial sampling by switching the focal spot between two positions on the anode plate during the scan. Although the focal spot mode does not play a role with respect to how the data are affected by the above mentioned corruption effects, it needs to be taken into account, if regularized sinogram restoration is to be applied to the data.
This work points out the subtle difference in processing that sinogram restoration for DFS requires, how it is correctly employed within the penalized maximum likelihood sinogram restoration algorithm, and what impact that has on image quality.
The raw data acquired during a computed tomography (CT) scan carry the unwanted traces of a number of adverse effects connected with the measurement setup and the acquisition process. To name a few, these include systematic errors like detector crosstalk and afterglow, fluctuations in tube power during the scan, but also statistical effects like photon noise. Most systematic effects can be cast into a linear model, providing a way for neutralizing the influence of these errors through deconvolution. However, this deconvolution process inevitably increases the image noise content. For low-dose scans, application of some kind of noise suppression algorithm is mandatory, in order to keep its disturbing influence on the reconstructed images in check. Since resolution and noise are antagonizing properties, noise suppression usually has the side effect of decreasing resolution. The interest in finding an algorithm that deals with this quandary in an optimal way is obvious. This work compares three deconvolution/denoising methods, identifying the one that performs best on a set of simulated data. The tested methods of combined sinogram deconvolution/denoising are based on (1) regularized matrix inversion, (2) straight matrix inversion plus adaptive filtering, and (3) deconvolution by a penalized maximum likelihood approach.
In-plane and axial noise/resolution measurements identified the penalized maximum-likelihood method as best suited for low-dose applications. The adaptive filter approach performed well, but did not retain as much resolution when going to higher smoothing levels. The analytic deconvolution, however, could not compete against the other two methods.