High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment

Abstract

We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free breathing, high spatial, and temporal resolution abdominal magnetic resonance imaging sequences. Based on a radial golden-angle acquisition trajectory, our method enables a multidimensional self-gating signal to be extracted from the <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space data for more accurate motion representation. The <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold. In the manifold, <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space profiles that represent similar respiratory positions are close to each other. Image reconstruction is performed by combining radial profiles with evenly distributed angles that are close in the manifold. Our method was evaluated on both 2-D and 3-D synthetic and <italic>in vivo</italic> data sets. On the synthetic data sets, our method achieved high correlation with the ground truth in terms of image intensity and virtual navigator values. Using the <italic>in vivo</italic> data, compared with a state-of-the-art approach based on the center of <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space gating, our method was able to make use of much richer profile data for self-gating, resulting in statistically significantly better quantitative measurements in terms of organ sharpness and image gradient entropy.