Zhuo-Xu Cui1, Yuanyuan Liu2, Qingyong Zhu1, Jing Cheng2, and Dong Liang1,2
1Research center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
1Research center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
In this paper, a physical decayed annihilation relation is proposed for MR parameter mapping, and a CNN based k-space interpolation method is further proposed via generalization.
Figure 4. The estimated T1ρ parameter
maps for selected cartilage ROIs with axial view overlaid on the reconstructed T1ρ-weighted images at TSL=5 ms with 6x
accelerated Possion sampling pattern ((b) and (c)) and with 7.6x accelerated
uniform sampling pattern ((d) and (e)). Visually, as can be seen from the
direction of the arrow, our proposed method is more accurate in estimating the T1ρ parameter
maps than SCOPE.
Figure 2. The framework of the
proposed k-space
interpolation network.
It consists of several stages and each stage is a component of three modules: learned k-space
interpolation module, data consistency module and learned joint TV module (from left to right in red box).
