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Abstract #1230

Rapid Automatic Quantification of Myocardial Blood Flow in Free-breathing Myocardial Perfusion MRI without the Need for Motion Correction: A Novel Spatio-temporal Deep Learning Approach

Zulma Sandoval1, John Van Dyke1, Prateek Malhotra1, Rohan Dharmakumar1, and Behzad Sharif1,2

1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2UCLA David Geffen School of Medicine, Los Angeles, CA, United States

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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