Photon-counting Spectral CT with De-noised Principal Component Analysis (PCA)

Huiqiao Xie; Yufei Liu; Thomas Thuering; Wenting Long; Xiangyang Tang

doi:10.1117/12.2534414

28 May 2019 Photon-counting Spectral CT with De-noised Principal Component Analysis (PCA)

Huiqiao Xie, Yufei Liu, Thomas Thuering, Wenting Long, Xiangyang Tang

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107212 (2019) https://doi.org/10.1117/12.2534414
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

While energy-integration spectral CT with the capability of material decomposition has been providing added value to diagnostic CT imaging in the clinic, photon-counting spectral CT is gaining momentum in research and development, with the potential of overcoming more clinically relevant challenges. In practice, the photon-counting spectral CT provides the opportunity for principal component analysis to effectively extract information from the raw data. However, the principal component analysis in spectral CT may suffer from high noise induced by photon starvation, especially in energy bins at the high energy end. Via phantom and small animal studies, we investigate the feasibility of principal component analysis in photon-counting spectral CT and the benefit that can be offered by de-noising with the Content-Oriented Sparse Representation method.

Citation Download Citation

Huiqiao Xie, Yufei Liu, Thomas Thuering, Wenting Long, and Xiangyang Tang "Photon-counting Spectral CT with De-noised Principal Component Analysis (PCA)", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107212 (28 May 2019); https://doi.org/10.1117/12.2534414

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