Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Reconstruction, cardiac cine imaging
Motivation: Cardiac cine MRI is the gold standard for cardiac functional assessment but requires acquiring several slices under multiple breath-holds, leading to limited number of cardiac phases, patient fatigue and misregistration between slices.
Goal(s): To develop a novel undersampled reconstruction based on Implicit Neural Representations (INR) to enable continuous cardiac cine MRI in a single heartbeat.
Approach: INRs allow implicitly regularized reconstruction of radial cardiac cine MRI without ECG gating. The proposed method is compared to a fully sampled acquisition and iterative SENSE in a healthy subject.
Results: The proposed approach shows comparable results to the fully sampled images but offering higher temporal resolution.
Impact: The proposed method allows implicitly regularized single heartbeat reconstruction of radial cardiac cine MRI without ECG gating, offerings potential improvements in cardiac cine acquisition efficiency and patient comfort.
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