The quality and quantitative accuracy of transmission CT images are affected by artifacts due to truncation of the projection data. In this study, the effect of data sampling on the quantitative accuracy of transmission CT images reconstructed from truncated projections has been investigated. Parallel-beam projections with different sets of acquisition and data sampling parameters were simulated. In deciding whether a set of parameters provided sufficient data sampling, use was made of the condition number obtained from the singular value decomposition of the projection matrix. The results of the study indicate that for noise-free data the truncation artifacts which are present in images reconstructed using iterative algorithms can be reduced or completely eliminated provided that the data sampling is sufficient, and an adequate number of iterations is performed. However, when a null space is present in the singular value decomposition, the iterative reconstruction methods fail to recover the object. The convergence of the reconstructed attenuation maps depends on the sampling and is faster as the number of angles and/or the number of projection bins is increased. Furthermore, the higher the degree of truncation the larger is the number of iterations required in order to obtain accurate attenuation maps. In the presence of noise, the number of iterations required for the best compromise of noise and image detail is decreased with increased noise level and higher degree of truncation, resulting in inferior reconstructions. Finally, the use of the body contour as support in the reconstructions resulted in quantitatively superior reconstructed images.
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|Publication status||Published - 2000|