A SImilarity-driven Multi-dimensional Binning Algorithm (SIMBA) was recently proposed for fast reconstruction of motion-consistent clusters for free-running whole-heart MRA acquisitions. Originally, only the most populated cluster was used for the reconstruction of a motion-suppressed image. In this work we investigated whether the redundancy of information among the clusters can be exploited to improve image quality. Specifically, an adapted XD-GRASP reconstruction and a multidimensional patch-based low-rank denoising algorithm were compared. Four different reconstructions were quantitatively evaluated and compared using ferumoxytol-enhanced free-running datasets from 10 pediatric and adult CHD patients. Information sharing resulted in significantly sharper anatomical features and increased image quality.