Myocardial perfusion cardiac MRI is widely used to functionally assess coronary artery disease. Although numerous acceleration techniques are used, improved spatio-temporal resolutions and coverage are desirable. Deep learning (DL) reconstruction has shown improvement over conventional reconstruction techniques at higher accelerations. Yet, SNR/contrast changes across perfusion dynamics hinder their generalization performance. In this work, we propose a multi-coil encoding operator that uses coil maps encoding structural information for physics-guided DL. This provides a uniform contrast at the network output across dynamics, leading to improved image quality compared to physics-guided DL with ESPIRiT coil maps, as well as conventional acceleration methods.
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