Cardiac T1 mapping has been shown to be a promising method for assessing different cardiomyopathies. Recently, native T1 mapping has been used to identify ischemic regions in coronary artery disease without the use of gadolinium-based contrast agents. Most cardiac T1 mapping methods require long breath holds during the acquisition sequence which can be difficult for patients particularly during exercise or pharmacologically induced stress. Here we proposed using attention-gated neural networks to reduce the acquisition time of native and post-contrast cardiac T1 mapping sequences without significant loss of quality.
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