Dynamic imaging of the lung and abdomen requires a trade-off between spatial resolution, temporal resolution, and total acquisition time. Due to its robustness against motion, the clinical use of free-breathing 3D  radial spoiled gradient echo (VIBE) imaging for abdominal applications has recently gained interest. It has also been shown that self-gating techniques enable the  reconstruction of different respiratory phases and compensation of respiratory motion in dynamic contrast-enhanced MRI (DCE-MRI). Typically, however, separate  acquisitions are required to capture the respiratory movement and dynamic perfusion. We show that a retrospective self-gating technique can be employed to extract all information from a single continuous free-breathing scan acquired with a stack-of-stars 3D VIBE sequence. This technique can be used to compute different sliding-window reconstructions that show a complete ‘virtual’ respiratory cycle with high spatial resolution, or, alternatively, a time series of the contrast-enhancement at a specified level of inspiration and with high temporal resolution.

Recently, novel Compressed Sensing methods have been proposed that promise very high undersampling factors in the spatial as well as temporal domain. However, it is unclear if these advanced reconstruction techniques preserve the true dynamics of the contrast enhancement, which otherwise could have significant impact on the quantitative analysis of the contrast-agent kinetics. In the latter case, a noisy image that reflects the true underlying dynamics would be more favorable than a beautified image that shows less noise and undersampling artifacts but deviates from the true course of contrast enhancement. To investigate these effects, we developed an extension to the well-known analytical Shepp-Logan phantom in the temporal dimension to simulate contrast enhancement in arteries and in healthy and pathological  tissue. Unlike image-based approaches, a k-space based phantom allows for accurate sampling along arbitrary trajectories. This enables assessing the influence  of k-space sampling strategies as well as the evaluation of reconstruction techniques for dynamic imaging. MATLAB source code for the phantom as well as for the generation of the contrast dynamics will be made available online here.

Radial sampling of k-space allows to deduce information about the respiratory cycle directly from the data (“self-gating”). 
Such signals are often biased  by imperfect gradient timings, leading to an angular modulation. For golden-angle trajectories with an angular increment of 111.25°, this causes high-frequency variations. Conventional band-pass filtering reduces the fluctuations but causes loss of temporal accuracy. We propose a robust filtering scheme to suppress the angular dependency in self-gating signals extracted from golden-angle radial MRI data. The proposed filter tolerates irregular breathing and preserves temporal resolution.