Author/Authors :
Wang, Yiran Department of Biomedical Engineering - Zhejiang University - Hangzhou, China , Chen, Zhifeng Department of Biomedical Engineering - Zhejiang University - Hangzhou, China , Wang, Jing Department of Biomedical Engineering - Zhejiang University - Hangzhou, China , Yuan, Lixia Department of Biomedical Engineering - Zhejiang University - Hangzhou, China , Xia, Ling Department of Biomedical Engineering - Zhejiang University - Hangzhou, China , Liu, Feng School of Information Technology and Electrical Engineering - The University of Queensland - Brisbane - QLD, Australia
Abstract :
The 𝑘-𝑡 principal component analysis (𝑘-𝑡 PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic
resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction
factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse 𝑘-𝑡 PCA that
combines the 𝑘-𝑡 PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional
𝑘-𝑡 PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled 𝑘-𝑡 space from
the original reconstructed 𝑘-𝑡 space. The proposed method is tested through both simulations and in vivo datasets with different
reduction factors. Compared to the standard 𝑘-𝑡 PCA algorithm, the sparse 𝑘-𝑡 PCA can improve the normalized root-mean-square
error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.
