Ma, J.
(Sch. of Biomed. Eng., Southern Med. Univ., Guangzhou, China)
,
Huang, J.
(Sch. of Biomed. Eng., Southern Med. Univ., Guangzhou, China)
,
Zhang, H.
(Sch. of Biomed. Eng., Southern Med. Univ., Guangzhou, China)
,
Feng, Q.
(Sch. of Biomed. Eng., Southern Med. Univ., Guangzhou, China)
,
Liang, Z.
(Dept. of Radiol., State Univ. of New York, Stony Brook, NY, USA)
,
Lu, H.
(Dept. of Biomed. Eng., Fourth Mil. Med. Univ., Xi'an, China)
,
Chen, W.
(Sch. of Biomed. Eng., Southern Med. Univ., Guangzhou, China)
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. Ho...
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.
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.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.