Keywords: Heart, Machine Learning/Artificial IntelligenceCombined cardiac MRI with exercise (Ex-CMR) is a stress test with promising applications. However, standard ECG-segmented cine imaging during exercise is challenging. Free-breathing ECG-free real time cine can be achieved with compressed sensing. Yet tradeoff remains between temporal and spatial temporal resolution. Thus, we sought to develop a highly accelerated high-frame-rate cine for Ex-CMR by accelerating spatial resolution using Resolution Enhancement Generative Adversarial Inline Network (REGAIN), followed by synthesizing new frames using Deformation ENcoding Transformer (DENT). REGAIN enabled 14-fold scan acceleration, DENT enabled 2-fold improvement in temporal resolution. We achieved spatiotemporal resolution of 1.9 × 1.9 mm2 and 16 ms.
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