Incorporating known information into image reconstruction algorithms for transmission tomography

We propose an alternating minimization (AM) image estimation algorithm for iteratively reconstructing transmission tomography images. The algorithm is based on a model that accounts for much of the underlying physics, including Poisson noise in the measured data, beam hardening of polyenergetic radiation, energy dependence of the attenuation coefficients and scatter. It is well-known that these nonlinear phenomena can cause severe artifacts throughout the image when high-density objects are present in soft tissue, especially when using the conventional technique of filtered back projection (FBP). If we assume no prior knowledge of the high-density object(s), our proposed algorithm yields much improved images in comparison to FBP, but retains significant streaking between the high-density regions. When we incorporate the knowledge of the attenuation and pose parameters of the high-density objects into the algorithm, our simulations yield images with greatly reduced artifacts. To accomplish this, we adapted the algorithm to perform a search at each iteration (or after every n iterations) to find the optimal pose of the object before updating the image. The final iteration returns pose values within 0.1 millimeters and 0.01 degrees of the actual location of the high-density structures.