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Background: RSN whole-heart CMRA is a technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). Methods: We applied fNAV to 500 data sets from a numerical simulation, 22 healthy volunteers, and 549 cardiac patients. We compared fNAV to RSN and respiratory resolved XD-GRASP reconstructions of the same data and recorded reconstruction times. Motion accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Vessel sharpness was measured using Soap-Bubble. Finally, image quality analysis was performed by a blinded expert reviewer who chose the best image for each data set. Results The reconstruction time for fNAV images was significantly higher than RSN (6.1 +/- 2.1 minutes vs 1.4 +/- 0.3, minutes, p<0.025) but significantly lower than XD-GRASP (25.6 +/- 7.1, minutes, p<0.025). There is high correlation between the fNAV, and reference displacement estimates across all data sets (0.73 +/- 0.29). For all data, fNAV lead to significantly sharper vessels than all other reconstructions (p < 0.01). Finally, a blinded reviewer chose fNAV as the best image in 239 out of 571 cases (p = 10-5). Conclusion: fNAV is a promising technique for improving free-breathing 3D radial whole-heart CMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions.