Itera- tive Reconstruction Framework for High-Resolution X-ray CT Data

Small animal medical imaging has become an important tool for researchers as it allows noninvasively screening animal models for pathologies as well as monitoring dis- ease progression and therapy response. Currently, clinical CT scanners typically use a Filtered Backprojection (FBP) based method for image reconstruction. This algorithm is fast and generally produces acceptable results, but has several drawbacks. Firstly, it is based upon line integrals, which do not accurately describe the process of X-ray attenuation. Secondly, noise in the projection data is not properly modeled with FBP. On the other hand, iterative algorithms allow the integration of more complicated sys- tem models as well as robust scatter and noise correction techniques. Unfortunately, the iterative algorithms also have much greater computational demands than their FBP counterparts. In this thesis, we develop a framework to support iterative reconstruc- tions of high-resolution X-ray CT data. This includes exploring various system models and algorithms as well as developing techniques to manage the significant computa- tional and system storage requirements of the iterative algorithms. Issues related to the development of this framework as well as preliminary results are presented.