  Dynamic Contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue
perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques
combining compressed sensing (CS) and parallel imaging with golden-angle radial
sampling have been developed to improve motion robustness with high spatial and
temporal resolution. These methods have demonstrated good diagnostic
performance in clinical setting, but the reconstruction quality will degrade at
high acceleration rates and overall reconstruction time remains long. In this
paper, we proposed a new parallel CS reconstruction model for DCE-MRI that
enforces flexible weighted sparse constraint along both spatial and temporal
dimensions. Weights were introduced to flexibly adjust the importance of time
and space sparsity, and we derived a fast thresholding algorithm which was
proven to be simple and efficient for solving the proposed reconstruction
model. Results on in vivo liver DCE datasets show that the proposed method
outperforms the state-of-the-art methods in terms of visual image quality
assessment and reconstruction speed without introducing significant temporal
blurring.
