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Abstract #2792

A deep learning model to enhance temporal information for the cardiac cine imaging

Tzu Cheng Chao1, Jacinta Browne1, Spencer Waddle2, Dinghui Wang1, and Tim Leiner1
1Mayo Clinic, Rochester, MN, United States, 2MRI R&D, Philips Healthcare, Rochester, MN, United States

Synopsis

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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Keywords