Deep Learning-based Automatic Perfusion Phase Identification for Dynamic T1-weighted Liver MRI

A deep learning-based approach for automatic identification of the perfusion phases in dynamic T1-weighted liver MRI is presented. First, an encoder model combined with two dense layers was trained to classify each image into pre-contrast, arterial, portal-venous, late, or hepatobiliary phase. In a second pass, classification errors are detected and adjusted, based on the expected occurrence order and relative timing to the arterial phase. The AI model reached sensitivities of 67% to 99%. Most common mis-classifications were confusions of the portal-venous or late phase with the adjacent phases. By the rule-based adjustments, the classification performance was raised to >95% accuracy.