Preliminary results on noise reduction using stackgrams for (low-dose) X-ray CT sinograms

In low-dose X-ray computed tomography (CT), it is necessary to reduce the excessive X-ray quantum noise of the acquired data. This is the case especially when the filtered back-projection method is used for image reconstruction from sinogram data. In this study, we compare the conventional radial filtering direction of the sinogram to the recently introduced angular stackgram filtering technique. Our experimental tests were performed on simulated low-dose CT data. We modelled the X-ray quantum noise using a Gaussian distribution with a signal dependent variance for numerical phantoms simulating stop-and-shoot CT acquisitions. The noise model has been reported elsewhere. This experimentally verified model describes calibrated projection data, i.e. the acquired data after CT system calibration (including logarithmic transform, beam hardening, etc.). For our quantitative tests, we employed both linear Gaussian low-pass filters and non-linear L-filters with Gaussian weights. The stackgram technique provides the best resolution-noise trade-off in comparison with the radial sinogram filtering method, and thus a potential filtering approach to low-dose CT sinograms.