Artifacts and sampling requirement in transmission CT reconstruction with truncated projection data

In recent years, the quantitative accuracy of reconstructed SPECT images has been enhanced by compensating for photon attenuation using attenuation maps obtained from transmission CT data. 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 parameters were simulated. In deciding whether a set of acquisition parameters (in terms of the number of linear and angular samples) provided sufficient sampling, use was made of the singular value decomposition of the projection matrix. The results of the study indicate that for noise-free data the ring artifact which is present in images reconstructed using iterative algorithms can be reduced or completely eliminated provided that the sampling is sufficient and an adequate number of iterations is performed. Reconstructions using the singular value decomposition were obtained and correlated very well with the reconstructions obtained using iterative algorithms. When the singular value decomposition indicated the presence of a null space, the iterative reconstruction methods failed to recover the object. The quantitative accuracy of the reconstructed attenuation maps depends on the sampling and is better as the number of angles and/or the number of projection bins is increased. Furthermore, the higher the degree of truncation the larger 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. Finally, the use of the body contour as support in the reconstructions resulted in quantitatively superior reconstructed images.