Improving the Accuracy of Cardiac T1 Maps with a Deep Learning-Based System: Virtual MOLLI Target and LocalNet

Motivation: Accurate cardiac T1 mapping is crucial for diagnosing heart conditions, yet patient motion can cause misaligned images. We aimed to address this with an automatic registration system. Goal(s): Develop and validate a high-precision automatic registration system for aligning MOLLI cardiac images. Approach: We created a system that integrates a GAN-generated virtual MOLLI target (VMT) and a deep-learning-based multi-modal registration method (DL) and applied it to a dataset, using the fitting quality index (FQI) for assessment. Results: Our findings indicate that while all three tested registration methods improved alignment. Our VMT+DL system consistently performed well in datasets with significant motion, while traditional methods faltered. Impact: The VMT+DL system offers a robust alternative for cardiac T1 mapping in clinical settings, where patient movement can compromise image registration. It ensures the reliability of diagnostic imaging, which is crucial for patient care in cardiology.