18 March 2015 Low rank approximation (LRA) based noise reduction in spectral-resolved x-ray imaging using photon counting detector
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Abstract
Spectral imaging with photon counting detectors has recently attracted a lot of interest in X-ray and CT imaging due to its potential to enable ultra low radiation dose x-ray imaging. However, when radiation exposure level is low, quantum noise may be prohibitively high to hinder applications. Therefore, it is desirable to develop new methods to reduce quantum noise in the acquired data from photon counting detectors. In this paper, we propose a new denoising algorithm to reduce quantum noise in data acquired using an ideal photon counting detector. The proposed method exploits the intrinsic low dimensionality of acquired spectral data to decompose the acquired data in a series of orthonormal spectral bases. The first few spectral bases contain object information while the rest of the bases contain primarily quantum noise. The separation of image content and noise in these orthogonal spatial bases provides a means to reject noise without losing image content. Numerical simulations were conducted to validate and evaluate the proposed noise reduction algorithm. The results demonstrated that the proposed method can effectively reduce quantum noise while maintaining both spatial and spectral fidelity.
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Yinsheng Li, Jiang Hsieh, Guang-Hong Chen, "Low rank approximation (LRA) based noise reduction in spectral-resolved x-ray imaging using photon counting detector", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 941212 (18 March 2015); doi: 10.1117/12.2081947; https://doi.org/10.1117/12.2081947
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