Motion correction with subspace-based self-navigation for combined angiography, perfusion and structural imaging

Abstract Motion artifacts are problematic in many MRI modalities. “Self-navigating” approaches are desirable, since no additional scan time or hardware is required. However, the generation of a navigator image, to estimate and correct motion, is difficult in cases where the tissue contrast is changing during the navigator acquisition window, such as in magnetization- prepared methods. Here we propose a subspace approach to reconstruct accurate navigators in the presence of time-varying tissue contrast and apply it to a combined angiography, perfusion and structural imaging method using a golden ratio 3D cones trajectory. This arterial spin labeling-based pulse sequence relies on subtraction of label and control images to isolate the relatively weak blood signal, making it particularly susceptible to motion corruption. An inversion pulse leads to time-varying tissue contrast across the readout train, but by reconstructing subspace coefficient maps directly, artifacts due to the varying contrast were alleviated. This resulted in high-quality navigator images that were subsequently registered to estimate and correct for motion. In addition, a split-update method was proposed to efficiently reconstruct from mismatched label/control k-space data with locally low rank regularization enforced on the difference image. The correction process was tested with numerical simulation and in vivo data from 8 healthy subjects with and without cued motion. In numerical simulation, the subspace- based navigator achieved an 84% reduction in RMSE of residual motion compared to without motion correction. In vivo, motion correction resulted in noise-like and background artifacts being greatly reduced and vessel sharpness being noticeably improved. Correlation of angiography, perfusion and structural images with motion-free reference images also increased by 159%, 53% and 12%, respectively, after motion correction. These results show that subspace-based navigators can effectively improve the motion robustness of MR imaging in contrast-varying acquisitions.