ABSTRACT Purpose To develop a unified image reconstruction framework that bridges real‐time and gated cardiac MRI, including quantitative MRI. Methods We introduce generative multitasking, which learns subject‐ and dataset‐specific implicit neural temporal bases from sequence timings and an interpretable latent space for cardiac and respiratory motion. Cardiac motion is modeled as a complex harmonic, with phase encoding timing and a latent amplitude capturing beat‐to‐beat functional variability, linking cardiac phase‐resolved (“gated‐like”) and time‐resolved (“real‐time‐like”) views. We implemented the framework using a conditional variational autoencoder (CVAE) and evaluated it for free‐breathing, non‐ECG‐gated radial GRE in three settings: steady‐state cine imaging, multicontrast T2prep/inversion‐recovery imaging, and dual‐flip‐angle T1/T2 mapping, compared with conventional multitasking. Results Generative multitasking provided flexible cardiac motion representation, enabling reconstruction of archetypal cardiac phase‐resolved cines (like gating) as well as time‐resolved series that reveal beat‐to‐beat variability (like real‐time imaging). Conditioning on the previous k‐space angle and modifying this term at inference removed eddy‐current artifacts without globally smoothing high temporal frequencies. For quantitative mapping, generative multitasking reduced intraseptal T1 and T2 coefficients of variation (CoV) compared with conventional multitasking (T1: 0.13 vs. 0.31; T2: 0.12 vs. 0.32; p < 0.001), indicating higher SNR. Conclusion Generative multitasking uses a CVAE with complex harmonic cardiac coordinates to unify gated and real‐time CMR within a single free‐breathing, non‐ECG‐gated acquisition. The framework allows flexible cardiac motion representation, suppresses trajectory‐dependent artifacts, and improves T1 and T2 mapping, suggesting a path toward cine, multicontrast, and quantitative imaging without separate gated and real‐time scans.
Fang et al. (Tue,) studied this question.