GPU-based iterative reconstruction with total variation minimization for micro-CT
Dynamic imaging with micro-CT often produces poorly-distributed sets of projections, and reconstructions of this data with filtered backprojection algorithms (FBP) may be affected by artifacts. Iterative reconstruction algorithms and total variation (TV) denoising are promising alternatives to FBP, but may require running times that are frustratingly long. This obstacle can be overcome by implementing reconstruction algorithms on graphics processing units (GPU). This paper presents an implementation of a family of iterative reconstruction algorithms with TV denoising on a GPU, and a series of tests to optimize and compare the ability of different algorithms to reduce artifacts. The mathematical and computational details of the implementation are explored. The performance, measured by the accuracy of the reconstruction versus the running time, is assessed in simulations with a virtual phantom and in an in vivo scan of a mouse. We conclude that the simultaneous algebraic reconstruction technique with TV minimization (SART-TV) is a time-effective reconstruction algorithm for producing reconstructions with fewer artifacts than FBP. © 2010 SPIE.
Johnston, SM; Johnson, GA; Badea, CT
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