Manifold-based models have been recently exploited for accelerating dynamic magnetic resonance imaging (dMRI). While manifold-based models have shown to be more efficient than conventional low-rank approaches, joint low-rank and sparsity-aware modeling still appears to be very efficient due to the inherent sparsity within dMR images. In this paper, we propose a joint manifold-learning and sparsity-aware framework for dMRI. The proposed method establishes a link between the recently developed manifold models and conventional sparsity-aware models. Dynamic MR images are modeled as points located on or close to a smooth manifold, and a novel data-driven manifold-learning approach, which preserves affine relation among images, is used to learn the low-dimensional embedding of the dynamic images. The temporal basis learnt from such an approach efficiently captures the inherent periodicity of dynamic images and hence sparsity along temporal direction is enforced during reconstruction. The proposed framework is validated by extensive numerical tests on phantom and in-vivo data sets.
Keywords: Dynamic image reconstruction; cardiac MRI; manifold learning; manifold regularization.