Exact image reconstruction with triple-source saddle-curve cone-beam scanning

Analytic reconstruction algorithms for triple-source CT with horizontal data truncation

PURPOSE

This paper explores a triple-source imaging method with horizontal data truncation to enlarge the field of view (FOV) for big objects.

METHODS

The study is conducted by using theoretical analysis, mathematical deduction, and numerical simulations. The proposed algorithms are implemented in c + + and matlab. While the basic platform is constructed in matlab, the computationally intensive segments are coded in c + +, which are linked via a mex interface.

RESULTS

A triple-source circular scanning configuration with horizontal data truncation is developed, where three pairs of x-ray sources and detectors are unevenly distributed on the same circle to cover the whole imaging object. For this triple-source configuration, a fan-beam filtered backprojection-type algorithm is derived for truncated full-scan projections without data rebinning. The algorithm is also extended for horizontally truncated half-scan projections and cone-beam projections in a Feldkamp-type framework. Using their method, the FOV is enlarged twofold to threefold to scan bigger objects with high speed and quality. The numerical simulation results confirm the correctness and effectiveness of the developed algorithms.

CONCLUSIONS

The triple-source scanning configuration with horizontal data truncation cannot only keep most of the advantages of a traditional multisource system but also cover a larger FOV for big imaging objects. In addition, because the filtering is shift-invariant, the proposed algorithms are very fast and easily parallelized on graphic processing units.