TY - GEN
T1 - Algorithm-based low-dose computed tomography image reconstruction
AU - Ma, J.
AU - Huang, J.
AU - Zhang, H.
AU - Feng, Q.
AU - Liang, Z.
AU - Lu, H.
AU - Chen, W.
PY - 2012
Y1 - 2012
N2 - Low-dose computed tomography (CT) reconstruction is a significant concern in CT imaging field. Currently, besides CT manufacturers adapted hardware techniques and optimized scan protocols to reduce the X-ray dose, algorithm-based low-dose CT reconstruction methods have been exploited extensively. However, for achieving high-quality algorithm-based low-dose CT reconstruction, there exist several challenges due to the excessive noise in low-dose projection data and the complex noise and artifacts characteristics in low-dose CT image. Statistical iterative reconstruction (SIR) methods have shown the potential to achieve a superior noise-resolution tradeoff as compared to analytical reconstruction techniques, however a main drawback of SIR is the computational burden associated with the multiple reprojection and back-projection operation cycles through the image domain. In this study, we propose an algorithm-based low-dose CT image reconstruction framework, which by making full use of the advantages of both the low-dose CT projection/sinogram data recovery and advanced edge-preserving CT image restoration. Simulated experimental results demonstrate that the present framework can yield image with better quality comparable to the obtained with the existing methods.
AB - Low-dose computed tomography (CT) reconstruction is a significant concern in CT imaging field. Currently, besides CT manufacturers adapted hardware techniques and optimized scan protocols to reduce the X-ray dose, algorithm-based low-dose CT reconstruction methods have been exploited extensively. However, for achieving high-quality algorithm-based low-dose CT reconstruction, there exist several challenges due to the excessive noise in low-dose projection data and the complex noise and artifacts characteristics in low-dose CT image. Statistical iterative reconstruction (SIR) methods have shown the potential to achieve a superior noise-resolution tradeoff as compared to analytical reconstruction techniques, however a main drawback of SIR is the computational burden associated with the multiple reprojection and back-projection operation cycles through the image domain. In this study, we propose an algorithm-based low-dose CT image reconstruction framework, which by making full use of the advantages of both the low-dose CT projection/sinogram data recovery and advanced edge-preserving CT image restoration. Simulated experimental results demonstrate that the present framework can yield image with better quality comparable to the obtained with the existing methods.
UR - https://www.scopus.com/pages/publications/84864190690
U2 - 10.1109/BHI.2012.6211721
DO - 10.1109/BHI.2012.6211721
M3 - 会议稿件
AN - SCOPUS:84864190690
SN - 9781457721779
T3 - Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012
SP - 856
EP - 857
BT - Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics
T2 - IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering
Y2 - 2 January 2012 through 7 January 2012
ER -