Purpose Cardiac MRI represents the gold standard to determine myocardial function. However, the current clinical standard protocol, a segmented Cartesian acquisition, is time‐consuming and can lead to compromised image quality in the case of arrhythmia or dyspnea. In this article, a machine learning–based reconstruction of undersampled spiral k‐space data is presented to enable free breathing real‐time cardiac MRI with good image quality and short reconstruction times. Methods Data were acquired in free breathing with a 2D spiral trajectory corrected by the gradient system transfer function. Undersampled data were reconstructed by a variational network (VN), which was specifically adapted to the non‐Cartesian sampling pattern. The network was trained with data from 11 subjects. Subsequently, the imaging technique was validated in 14 subjects by quantifying the difference to a segmented reference acquisition, an expert reader study, and by comparing derived volumes and functional parameters with values obtained using the current clinical gold standard. Results The scan time for the entire heart was below 1 min. The VN reconstructed data in about 0.9 s per image, which is considerably shorter than conventional model‐based approaches. The VN furthermore performed better than a U‐Net and not inferior to a low‐rank plus sparse model in terms of achieved image quality. Functional parameters agreed, on average, with reference data. Conclusions The proposed VN method enables real‐time cardiac imaging with both high spatial and temporal resolution in free breathing and with short reconstruction time.

Data-driven reconstruction of undersampled raw data has gained more and more importance in recent years. Due to the non-linear and non-stationary transform characteristics of these imaging methods, objective image quality assessment is difficult. We propose a heuristic approach based on local point spread functions and multiple replica reconstructions, to enable the derivation of resolution- and g-factor-maps for individual images. The method is exemplarily applied in T1- and T2-weighted images of the brain, using a UNet and a Variational Network trained with data from the fastMRI project.