For cardiac cine imaging, the center of k-space (DC component) in each spoke (Fig. 2a), which reflects the change in average signal level due to changes of the volume of lung and heart in the excited slab, was used to extract information about physiological motion over time (33 (link)). Information from multiple coils was used to obtain separate signals representing respiratory or cardiac motion, as shown in Supporting Figure S2b. Conceptually, the motion signal from the coil nearest to the heart provides predominantly cardiac motion information, and the motion signal from the coil nearest to the diaphragm provides predominantly respiratory motion information. Because these motions are known to have different frequency contents, the motion signal in the coil-element with the highest peak in the frequency range of 0.1–0.5 Hz was automatically selected to represent respiratory motion; and the motion signal in the coil-element with the highest peak in the frequency range of 0.5–2.5 Hz was automatically selected to represent cardiac motion. A filtering procedure can be performed on the detected motion signals for denoising (16 (link)). Figure 2b shows an example of detected motion signals and Figure 2c shows the corresponding frequency information. End-systolic motion-states were identified as the valleys in the cardiac motion signal and thus any abnormal cardiac cycles, in case of arrhythmias, can be identified according to the difference between cycle lengths for rejection or a separate reconstruction. Given the selected motion signals, the continuously acquired 2D cardiac dataset can be sorted into an expanded dataset containing two dynamic dimensions, representing predominantly cardiac and respiratory motions, respectively. Specifically, the continuously acquired golden-angle radial dataset were first sorted into a dynamic cardiac series by grouping several consecutive spokes (e.g., 15 spokes) as one cardiac phase (Fig. 2d). All the cardiac cycles, identified using the cardiac motion signal, were then sorted into an expanded dataset to generate an extra respiratory state dimension tR (Fig. 2e), so that sparsity along both cardiac and respiratory dimensions can be exploited in the compressed sensing reconstruction.