Stationary source inverse-geometry CT (SS-IGCT) has been proposed as a new system architecture that has several key
advantages over traditional cone beam CT (CBCT). One advantage is the potential for acquiring a large volume of
interest with minimal cone-beam artifacts and with very high temporal resolution. We anticipate that SS-IGCT will use
large, stationary source arrays, with gaps in between separate source array modules. These gaps make reconstruction
challenging because most analytic reconstruction algorithms assume a continuous source trajectory. SS-IGCT is capable
of producing the same dataset as a traditional scanner taking multiple overlapping axial scans, but with segments of the
views missing from each axial scan because of gaps. We propose the following, two-stage volumetric reconstruction
algorithm. In the first stage, the missing rays are estimated in a spatially varying fashion using available data and
geometric considerations, and reconstruction proceeds with standard algorithms. The missing data are then re-estimated
by a forward projection step. These new estimates are quite good and the reconstruction can be performed again using
any algorithm that supports multiple parallel axial scans. Although inspired by iterative reconstruction, our algorithm
only needs one "iteration" of forward- and back-projection in practice and is efficient. Simulations of a thorax phantom
were performed showing the efficacy of this technique and the ability of SS-IGCT to suppress cone-beam artifacts
compared to conventional CBCT. The noise and resolution characteristics are comparable to that of CBCT.
Scott S. Hsieh,
Norbert J. Pelc,
"A volumetric reconstruction algorithm for stationary source inverse-geometry CT", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133N (3 March 2012); doi: 10.1117/12.912490; https://doi.org/10.1117/12.912490
Scott S. Hsieh, Norbert J. Pelc, "A volumetric reconstruction algorithm for stationary source inverse-geometry CT," Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133N (3 March 2012);