It can be argued that the most significant technical impediment for wider clinical adoption of fully-quantitative cardiac perfusion MRI is the lack of a fully-automatic post-processing workflow across all scanner platforms. In this work, we present an initial proof-of-concept based on a deep-learning approach for quantification of myocardial blood flow that eliminates the need for motion correction, hence enabling a rapid and platform-independent post-processing framework. This is achieved by optimizing/training a cascade of deep convolutional neural networks to learn the common spatio-temporal features in a dynamic perfusion image series and use it to jointly detect the myocardial contours across all dynamic frames in the dataset.
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