Keywords: Machine Learning/Artificial Intelligence, Cardiovascular
Motivation: Evaluation of cardiac function with cine imaging remains long and requires repeated breath-holds that are sometimes corrupted with artifacts if patients have non-sinus rhythm or difficulty in breath-holding.
Goal(s): To develop a deep learning method with spatio-channel regularization with multi-channel k-space reconstruction for accelerated cine imaging.
Approach: Coil-self consistency based deep learning (DL) was implemented with 3D regularization across spatial and channel dimensions in contrast to single coil-combined image used in sensitivity encoding (SENSE).
Results: Our approach at 5-fold acceleration showed quantitative improvements over SENSE-based DL on retrospectively accelerated data and showed good agreement with left ventricular (LV) measurements on prospectively accelerated data.
Impact: The spatio-channel regularized DL reconstruction shortens the scan time by a factor of 5, leading to fewer breath-holds and 2–3-minute scans. This can greatly benefit patients struggling with breath-holding and accelerate the overall scan time.
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