Background: CMR allows investigating cardiac contraction, rotation and torsion non-invasively by the use of tagging sequences. Three-dimensional tagging has been proposed to cover the whole-heart but data acquisition requires three consecutive breath holds and hence demands considerable patient cooperation. In this study we have implemented and studied k-t undersampled cine 3D tagging in conjunction with k-t PCA reconstruction to potentially permit for single breath-hold acquisitions. Methods: The performance of undersampled cine 3D tagging was investigated using computer simulations and in-vivo measurements in 8 healthy subjects and 5 patients with myocardial infarction. Fully sampled data was obtained and compared to retrospectively and prospectively undersampled acquisitions. Fully sampled data was acquired in three consecutive breath holds. Prospectively undersampled data was obtained within a single breath hold. Based on harmonic phase (HARP) analysis, circumferential shortening, rotation and torsion were compared between fully sampled and undersampled data using Bland-Altman and linear regression analysis. Results: In computer simulations, the error for circumferential shortening was 2.8 ± 2.3% and 2.7 ± 2.1% for undersampling rates of R = 3 and 4 respectively. Errors in ventricular rotation were 2.5 ± 1.9% and 3.0 ± 2.2% for R = 3 and 4. Comparison of results from fully sampled in-vivo data acquired with prospectively undersampled acquisitions showed a mean difference in circumferential shortening of −0.14 ± 5.18% and 0.71 ± 6.16% for R = 3 and 4. The mean differences in rotation were 0.44 ± 1.8° and 0.73 ± 1.67° for R = 3 and 4, respectively. In patients peak, circumferential shortening was significantly reduced (p < 0.002 for all patients) in regions with late gadolinium enhancement. Conclusion: Undersampled cine 3D tagging enables significant reduction in scan time of whole-heart tagging and facilitates quantification of shortening, rotation and torsion of the left ventricle without adding significant errors compared to previous 3D tagging approaches.