Keywords: Machine Learning/Artificial Intelligence, Cardiovascular, deep learning; spiral; real-time CMR
Motivation: Real-time imaging methods are useful for patients with limited breathhold capacity or arrhythmias, but are typically limited to 2D scans that prevent evaluation of wall motion in 3D over the heart.
Goal(s): The goal of this project is to develop a technique for 3D real-time (free-breathing ungated) cine imaging.
Approach: The proposed method combines a highly undersampled 3D stack-of-spirals trajectory with a deep image prior reconstruction, which does not require ground truth training data.
Results: Real-time 3D imaging is demonstrated in healthy subjects with temporal resolutions of 36ms per volume at 1.5T and 58ms per volume at 0.55T.
Impact: Real-time 3D imaging could enable streamlined cardiac MRI exams, with whole-heart 3D cine images obtained in 10s without breathholds or gating. This technique may also simplify quantification compared to 2D real-time methods, since motion is synchronized over all partitions.
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