Impact of redundant ray weighting on motion artifact in a statistical iterative reconstruction framework

Yinghua Tao; Jie Tang; Michael Speidel; Guang-Hong Chen

doi:10.1117/12.2043824

19 March 2014 Impact of redundant ray weighting on motion artifact in a statistical iterative reconstruction framework

Yinghua Tao, Jie Tang, Michael Speidel, Guang-Hong Chen

Proceedings Volume 9033, Medical Imaging 2014: Physics of Medical Imaging; 90332X (2014) https://doi.org/10.1117/12.2043824
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

In recent years, iterative reconstruction methods have been investigated extensively with the aim of reducing radiation dose while maintaining image quality in CT exams. In such a case, redundant data is usually available. In conventional FBP-type reconstructions, redundant data has to be carefully treated by applying a redundant weighting factor, such as Parker weighting. However, such a redundant weight has not been fully studied in a statistical iterative reconstruction framework. In this work, both numerical simulations and in vivo data sets were analyzed to study the impact of redundant weighting schemes on the reconstructed images for both static and moving objects. Results demonstrated that, for a static object, there was no obvious difference in the iterative reconstructions using different redundant weighting schemes, because the redundant data was consistent, and therefore, they all converged to the same solution. On the contrary, for a moving object, due to the inconsistency of the data, different redundant weighting schemes converged to different solutions, depending on the weight given to the data. The redundant weighting, if appropriately selected, can reduce motion-induced artifacts.

Citation Download Citation

Yinghua Tao, Jie Tang, Michael Speidel, and Guang-Hong Chen "Impact of redundant ray weighting on motion artifact in a statistical iterative reconstruction framework", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90332X (19 March 2014); https://doi.org/10.1117/12.2043824

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KEYWORDS

In vivo imaging

Data acquisition

Numerical simulations

Computed tomography

Fluctuations and noise

Infrared imaging

Statistical analysis

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