This is the first report on a new fast statistical iterative reconstruction algorithm for conebeam with a circular source
trajectory, accelerated by InstaRecon's fast O(N3logN) hierarchical cone beam backprojection1 and reprojection
algorithms. We report on the results of image quality and run-time comparisons with iterative algorithms based on
conventional backprojection and reprojection. We demonstrate that the iterative algorithm introduced here can provide
image quality indistinguishable from an iterative algorithm using conventional BP/RP operators, while providing almost
a 10x speedup in reconstruction rates. Combining the 10x algorithmic acceleration with additional hardware acceleration
by FPGA, Cell, or GPU implementation, this work indicates the feasibility of iterative reconstruction algorithms for dose
reduction and image quality improvement in routine CT practice, at competitive speeds and affordable cost.
It is often the case in tomography that a scanner is unable to collect a full set of projection data. Reconstruction
algorithms that are not set up to handle this type of problem can lead to artifacts in the reconstructed images
because the assumptions regarding the size of the image space and/or data space are violated. In this study,
we apply two recently developed geometry-independent methods to fully 3D multi-slice spiral CT image reconstruction.
The methods build upon an existing statistical iterative reconstruction algorithm developed by our
group. The first method reconstructs images without the missing data, and the second method seeks to jointly
estimate the missing data and attenuation image. We extend the existing results for the 2D fan-beam geometry
to multi-slice spiral CT in an effort to investigate some challenges in 3D, such as the long object problem. Unlike
the original formulation of the reconstruction algorithms, a regularization term was added to the objective
function in this work. To handle the large number of computations required by fully 3D reconstructions, we
have developed an optimized parallel implementation of our iterative reconstruction algorithm. Using simulated
and clinical datasets, we demonstrate the effectiveness of the missing data approaches in improving the quality
of slices that have experienced truncation in either the transverse or longitudinal direction.