A deep learning-based approach for automatic myocardial T1 map analysis

Motivation: In cardiovascular magnetic resonance, T1 analysis is generally completed in a manual manner, which is a labor-intensive and time-consuming procedure and could be automated by deep-learning algorithms. Goal(s): This study aims to develop a deep learning-based technique for directly analyzing T1 for a T1 map. Approach: We built a cascaded neural network to predict T1 of the left-ventricle myocardium, septum, blood, and AHA segments and generate LV mask to improve performance. Results: The automatic T1 analysis performed by the proposed approach had good agreement with manual analysis. The mean difference was ~10 ms. Impact: The proposed approach could automatically estimate the left ventricle, septum, and blood T1. Along with automatic motion correction and T1 calculation algorithms, the proposed approach could further simplify and improve the automatization of the workflow of myocardial T1 mapping examination.