Temporal feature prior-aided separated reconstruction method for low-dose dynamic myocardial perfusion computed tomography.


Chen Z(1)(2), Zeng D(3), Huang Z(1)(2), Ma J(4), Gu Z(5), Yang Y(1)(2), Liu X(1)(2), Zheng H(1)(2), Liang D(1)(2), Hu Z(1)(2)(5).
Author information:
(1)Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.
(2)Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China.
(3)College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China.
(4)School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.
(5)Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518107, People's Republic of China.


Dynamic myocardial perfusion computed tomography (DMP-CT) is an effective medical imaging technique for coronary artery disease diagnosis and therapy guidance. However, the radiation dose received by the patient during repeated CT scans is a widespread concern of radiologists because of the increased risk of cancer. The sparse few-view CT scanning protocol can be a feasible approach to reduce the radiation dose of DMP-CT imaging; however, an advanced reconstruction algorithm is needed. In this paper, a temporal feature prior-aided separated reconstruction method (TFP-SR) for low-dose DMP-CT images reconstruction from sparse few-view sinograms is proposed. To implement the proposed method, the objective perfusion image is divided into the baseline fraction and the enhancement fraction introduced by the arrival of the contrast agent. The core of the proposed TFP-SR method is the utilization of the temporal evolution information that naturally exists in the DMP-CT image sequence to aid the enhancement image reconstruction from limited data. The temporal feature vector of an image pixel is defined by the intensities of this pixel in the pre-reconstructed enhancement sequence, and the connection between two related features is calculated via a zero-mean Gaussian function. A prior matrix is constructed based on the connections between the extracted temporal features and used in the iterative reconstruction of the enhancement images. To evaluate the proposed method, the conventional filtered back-projection algorithm, the total variation regularized PWLS (PWLS-TV) and the prior image constrained compressed sensing are compared in this paper based on studies on a digital extended cardiac-torso (XCAT) thoracic phantom and a preclinical porcine DMP-CT data set that take image misregistration into account. The experimental results demonstrate that the proposed TFP-SR method has superior performance in sparse DMP-CT images reconstruction in terms of image quality and the analyses of the time attenuation curve and hemodynamic parameters.