Keywords: Diagnosis/Prediction, Quantitative Imaging, Cardiac T1-rho mapping; automated analysis
Motivation: Contrast agent-free myocardial T1-rho (T1ρ) mapping has shown promise in myocardial injury quantification. However, the lack of analysis tools hinders its clinical use and induces increased workload and operator variability.
Goal(s): To explore the feasibility and benefits of clinically-integrated artificial intelligence-driven analysis of myocardial T1ρ mapping.
Approach: The automated process combines left ventricular wall segmentation, right ventricular insertion point detection and the creation of a 16-segment American Heart Association model for segmental T1ρ values analysis.
Results: Automated T1ρ mapping showcased strong agreement with manual processing, enhanced with time efficiency.
Impact: Artificial intelligence-driven analysis of myocardial T1-rho mapping exhibits strong agreement with manual processing, bolstered by time efficiency. This approach shows promise for the rapid and non-invasive assessment of heart disease without the need for contrast agents.
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