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Proceedings Paper

Artifact reduction in truncated CT using sinogram completion
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Paper Abstract

Truncation of projection data in CT produces significant artifacts in the reconstruction process due to non-locality of the Radon transform. In this paper, we present a method for reducing these truncation artifacts by estimating features that lie outside the region of interest (ROI) and using these features to complete the truncated sinogram. Projection images of an object are obtained. A sinogram is obtained by stacking profile data from all projection angles. A simulated truncated sinogram is generated by setting pixel values outside an ROI to zero. The truncated sinogram is then transformed into a (radius, phase) image, with pixel values in what we term as the Polar representation (PR) image corresponding to the minimum value along sine curves given by x = r*cos(projection angle + phase). The PR image contains data for radii greater than the ROI radius. Pixel values outside the ROI in the completed sinogram are determined as follows. For each pixel in the PR image, a sine curve is generated in the completed sinogram image outside the ROI, having the same pixel value as that of the PR image for that radius and phase. Successive sine curves are laid and the values of each are summed. The intensity outside is then equalized to the intensity inside the ROI. The completed sinogram is then reconstructed, to obtain completed reconstruction. The percentage error in the difference image between the full FOV reconstruction and the corresponding completed reconstruction and the extrapolated-average reconstruction are 1.1% and 3.3% respectively. This indicates that the completed reconstruction is closer to full FOV reconstruction. Thus, the sinogram completion can be used to improve reconstructions from truncated data.

Paper Details

Date Published: 29 April 2005
PDF: 8 pages
Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.595450
Show Author Affiliations
Ravishankar Chityala, SUNY/Buffalo (United States)
Erie County Medical Ctr. (United States)
Kenneth R. Hoffmann, SUNY/Buffalo (United States)
Stephen Rudin, SUNY/Buffalo (United States)
Erie County Medical Ctr. (United States)
Daniel R. Bednarek, SUNY/Buffalo (United States)
Erie County Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 5747:
Medical Imaging 2005: Image Processing
J. Michael Fitzpatrick; Joseph M. Reinhardt, Editor(s)

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