18 March 2015 Statistical model based iterative reconstruction in myocardial CT perfusion: exploitation of the low dimensionality of the spatial-temporal image matrix
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Abstract
Time-resolved CT imaging methods play an increasingly important role in clinical practice, particularly, in the diagnosis and treatment of vascular diseases. In a time-resolved CT imaging protocol, it is often necessary to irradiate the patients for an extended period of time. As a result, the cumulative radiation dose in these CT applications is often higher than that of the static CT imaging protocols. Therefore, it is important to develop new means of reducing radiation dose for time-resolved CT imaging. In this paper, we present a novel statistical model based iterative reconstruction method that enables the reconstruction of low noise time-resolved CT images at low radiation exposure levels. Unlike other well known statistical reconstruction methods, this new method primarily exploits the intrinsic low dimensionality of time-resolved CT images to regularize the reconstruction. Numerical simulations were used to validate the proposed method.
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Yinsheng Li, Kai Niu, Guang-Hong Chen, "Statistical model based iterative reconstruction in myocardial CT perfusion: exploitation of the low dimensionality of the spatial-temporal image matrix", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94123N (18 March 2015); doi: 10.1117/12.2081944; https://doi.org/10.1117/12.2081944
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