Keywords: Heart Failure, Cardiovascular, High Temporal Resolution Cine
Motivation: MR-Imaging of rapid processes, such as the beating heart, typically relies on segmented acquisitions. Improving temporal resolution often requires extended acquisition windows, potentially increasing the burden on patients.
Goal(s): A highly-efficient spiral bSSFP acquisition scheme, combined with a deep learning-based reconstruction is employed to reduce acquisition times for high-temporal-resolution cine imaging.
Approach: Spiral acquisitions were performed on a subject with regular heartbeat, enabling creation of segmented, undersampled cine frames with temporal resolutions of 3.7$$$\,$$$ms, 7.4$$$\,$$$ms and 11.1$$$\,$$$ms. Additionally, real-time frames were acquired with a temporal resolution of 48.1$$$\,$$$ms.
Results: The proposed approach achieves improved accuracy in depicting cardiac motion compared to real-time imaging.
Impact: With undersampled spiral bSSFP acquisitions and a disentanglement-based reconstruction method high temporal resolution cardiac cine imaging can be acquired with up to 3.7$$$\,$$$ms/frame, potentially improving accuracy in left ventricular strain analysis and cardiac function assessment in subjects with regular heartbeat.
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