General Cone-Beam Reconstruction With the Algebraic Reconstruction Technique (ART)

Cone-beam computed tomography (CT) is an emerging imaging technology. It provides all projections needed for three-dimensional (3D) reconstruction in a single spin of the X- ray source-detector pair. This facilitates fast, low-dose data acquisition, which is required for the imaging of rapidly moving objects, such as the human heart, as well as for intra-operative CT applications. In these scenarios, the number of projections is usually sparse. Current cone-beam reconstruction algorithms mainly employ the Filtered-Backprojection (FBP) approach, which has difficulties when the projection set is limited. In this work, a different class of reconstruction algorithm is studied: the algebraic reconstruction methods. Algebraic reconstruction starts from an initial guess for the reconstructed object and then performs a sequence of iterative grid projections and correction backprojections until the reconstruction has converged. Algebraic methods have reportedly a number of advantages over FBP, such as better noise tolerance and the better handling of sparse and non-uniformly distributed projection datasets. So far, the main repellant for using algebraic methods in routine clinical situations was their slow speed. This work provides solutions for this pressing problem. Furthermore, it also applies, for the first time, algebraic methods in the context of general low-contrast cone-beam tomography. This new context poses several challenges, both for reconstruction quality and speed.

The contributions of this work are as follows (see numbered publications listed below)

Here are the publications that describe these contributions: Here is a talk on cone-beam ART that I gave at the University of Pennsylvania in May 2000.

Finally, here is my dissertation that embraces all this material, some in more detail, some in less: "Fast and Accurate Three-Dimensional Reconstruction from Cone-Beam Projection Data Using Algebraic Methods"

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