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arXiv · 1011.4630

A constrained, total-variation minimization algorithm for low-intensity X-ray CT

Emil Y. Sidky·Yuval Duchin·Christer Ullberg·Xiaochuan Pan
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Abstract

Purpose: We develop an iterative image-reconstruction algorithm for application to low-intensity computed tomography (CT) projection data, which is based on constrained, total-variation (TV) minimization. The algorithm design focuses on recovering structure on length scales comparable to a detector-bin width. Method: Recovering the resolution on the scale of a detector bin, requires that pixel size be much smaller than the bin width. The resulting image array contains many more pixels than data, and this undersampling is overcome with a combination of Fourier upsampling of each projection and the use of constrained, TV-minimization, as suggested by compressive sensing. The presented pseudo-code for solving constrained, TV-minimization is designed to yield an accurate solution to this optimization problem within 100 iterations. Results: The proposed image-reconstruction algorithm is applied to a low-intensity scan of a rabbit with a thin wire, to test resolution. The proposed algorithm is compared with filtered back-projection (FBP). Conclusion: The algorithm may have some advantage over FBP in that the resulting noise-level is lowered at equivalent contrast levels of the wire.

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Emil Y. Sidky, Yuval Duchin, Christer Ullberg, Xiaochuan Pan. 2010-11-21. A constrained, total-variation minimization algorithm for low-intensity X-ray CT. https://doi.org/10.1118/1.3560887

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