Na Zhang1, 2, Guanghua Song1, 2, Weiqi Liao1, 2, Weijie Tao1, 2, Leslie Ying3, Dong Liang1, 2
1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; 2Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China; 3Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
The emerging CS-based methods have shown promising performance for accelerating DCE-MRI. In this work, the potential of k-t Iterative Support Detection (k-t ISD, a recently proposed dynamic imaging method based on CS with partial known support theory) in accelerating DCE-MRI is investigated and compared to the sliding window (SW) method. The superiority of this method to conventional CS methods has been demonstrated on cardiac cine imaging. The reconstruction results and statistical analysis indicate that k-t ISD can faithfully reconstruct the uptake curves and improve temporal resolution of DCE-MRI without compromising the spatial resolution, when a high net reduction factor is used.