Keywords: Myocardium, Myocardium, noncontrast, perfusion, deep learning
Motivation: Cardiac arterial spin labeling (ASL) method is sensitive to noise (system and physiology), which may lead to inaccurate MBF measurement.
Goal(s): A cardiac MRI arterial spin labeling method was developed with assistance of a deep learning networks (DeepCASL) to improve image quality and measurement accuracy.
Approach: The performance of the DeepCASL method was evaluated in a canine model of coronary arterial disease by comparing and correlating with MBF determined by microsphere measurements.
Results: The validation study revealed moderate to strong correlations in absolute myocardial blood flow values between MRI and microsphere reference methods.
Impact: This new DeepCASL technique opens a door for clinical applications of noncontrast cardiac perfusion as a screen tool for reliable diagnosis of perfusion deficit in a variety of cardiomyopathy disorders.
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