Keywords: Diagnosis/Prediction, AI/ML Software, B1+ Map, AI, ML, Prediction, GAN
Motivation: Transmit radiofrequency field (B1+) maps are typically needed for quantitative relaxation mapping but are not normally acquired in clinical studies.
Goal(s): We developed a machine learning model to predict B1+ maps from standard clinical brain images.
Approach: A Generative Adversarial Network (GAN) was trained on combinations of T1, T2 and proton density (PD) weighted images and used to predict brain B1+ maps in comparison to measured B1+ maps in a large dataset.
Results: The predicted B1+ maps closely matched the ground truth in both regional and global analyses, with best agreement in central regions.
Impact: Prediction of B1+ maps from standard clinical brain images will enable more widespread use of quantitative MRI in clinical settings where B1+ maps are not typically acquired.
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