Improved 𝑘-𝑡 PCA Algorithm Using Artificial Sparsity in Dynamic MRI

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.