Statistical modeling challenges in model-based reconstruction for x-ray CT

Ruoqiao Zhang; Aaron Chang; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman

doi:10.1117/12.2013231

19 February 2013 Statistical modeling challenges in model-based reconstruction for x-ray CT

Ruoqiao Zhang, Aaron Chang, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman

Proceedings Volume 8657, Computational Imaging XI; 86570S (2013) https://doi.org/10.1117/12.2013231
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Model- based iterative reconstruction (MBIR) is increasingly widely applied as an improvement over conventional, deterministic methods of image reconstruction in X-ray CT. A primary advantage of MBIR is potentially dras tically reduced dosage without diagnostic quality loss. Early success of the method has naturally led to growing numbers of scans at very low dose, presenting data which does not match well the simple statistical models heretofore considered adequate. This paper addresses several issues arising in limiting cases which call for refine ment of standard data models. The emergence of electronic noise as a significant contributor to uncertainty, and bias of sinogram values in photon-starved measurements are demonstrated to be important modeling problems in this new environment. We present also possible ameliorations to several of these low-dosage estimation issues.

Citation Download Citation

Ruoqiao Zhang, Aaron Chang, Jean-Baptiste Thibault, Ken Sauer, and Charles Bouman "Statistical modeling challenges in model-based reconstruction for x-ray CT", Proc. SPIE 8657, Computational Imaging XI, 86570S (19 February 2013); https://doi.org/10.1117/12.2013231

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