Data-driven clustering for ECG-free cine MRI with robustness against irregular cardiac motion

Both ECG-gating and self-gated approaches are used for cardiac motion binning in segmented cardiac cine MRI. Typical self-gated methods reduce ECG-dependency by assuming periodic cardiac motion, but may be less reliable in the presence of irregular cardiac motion. We propose a novel clustering algorithm that incorporates regularization in both temporal and cluster dimension to provide robustness against irregular cardiac motion in complex arrhythmias such as atrial fibrillation and premature ventricular contraction. Compared with images from k-means clustering, initial validation using the modified algorithm shows higher image quality scores with comparable single-to-noise ratio (SNR) and image sharpness.