Acceleration of Perfusion MRI using Adaptive Artificial Sparsity
Xiaoying Cai 1 , Feng Huang 2 , Kui Ying 3 , Chun Yuan 4 , and Huijun Chen 4
Biomedical Engineering, University of
Virginia, Charlottesville, Virginia, United States,
Healthcare, Gainesville, FL, United States,
of Engineering Physics, Tsinghua University, Beijing,
for Biomedical Imaging Research, Beijing, China
We proposed a new framework for reconstruction of
accelerating perfusion imaging. By prediction intensity
change of perfusion images using pre-contrast image and
adaptive weights , the sparsity of dynamic images was
strengthened, which benefits the following regular
imaging reconstruction (k-t GRAPPA, k-t PCA or k-t SLR).
The framework was tested with black blood vessel wall
imaging data. The results showed that our new framework
could result in better image reconstruction and improved
kinetic parameter fitting for all tested reconstruction
methods when acceleration factors were high.
This abstract and the presentation materials are available to members only;
a login is required.