Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Temporal superresolution
Motivation: Cine CMR is essential in the diagnosis of cardiac structure and function, but its long breath-holding time can be a burden for patients. Reducing temporal resolution allows reducing scan time but introduces artifacts.
Goal(s): This study proposes a deep learning model to improve image quality of fast, low temporal resolution cine CMR.
Approach: A cascaded neural network is implemented with the main cascade to recover signals in the y-t domain and the second cascade to eliminate artifacts in the x-y domain.
Results: The model can restore image quality of the low temporal resolution series on a variety of field strengths(0.6T, 1.5T, and 3.0T).
Impact: The proposed deep-learning based temporal interpolation for the cine CMR is independent of and compatible with existing acceleration strategies such as SENSE, Compressed Sensing and GRAPPA allowing a further increase in acceleration rate without the need for the raw data.
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