REGULARIZED IMAGE RECONSTRUCTION FOR PS MODEL-BASED CARDIOVASCULAR MRI

摘要

Real-time cardiovascular MRI is a useful and challenging dynamic imaging application. The partial separability (PS) model enables reconstruction of dynamic cardiac images from highly undersampled (>k, t)-space data. However, the underlying PS model-based reconstruction problem is ill-conditioned, so regularization is often necessary to stabilize its solution. It has been shown that ℓ1 regularization is useful for finding sparse solutions, and ℓ2 regularization is widely used to incorporate anatomical constraints. An important practical question is which regularization scheme to use for PS model-based cardiovascular imaging. We address this problem by implementing both schemes and evaluating their performances in terms of reconstruction error, image artifacts, image noise, computation time, and performance characterizability. The ℓ1-regularized results exhibit lower reconstruction error, artifact energy, and noise variance, while ℓ2 regularization is much faster and produces predictable reconstruction results. This study indicates that the ℓ1 scheme is preferable when image quality is the main concern.