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Rapid Multi-Parametric Quantitative MRI via Deep Learning-Based Synthetic-to-Real Reconstruction and 3D SSFP-MOLED Imaging
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DOI：10.1016/j.neuroimage.2026.121985.png)
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Abstract
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• 3D SSFP-MOLED enables multi-parametric mapping in M0, T1, T2, T2*, B1+, and ΔB0 in three minutes with only 2x parallel acquisition acceleration. • Synthetic training data used in 3D SSFP-MOLED to compensate for the absence of paired samples for supervised learning • 3D SSFP-MOLED has proven reliability and accuracy in phantoms, healthy volunteers, brain tumor and hemorrhage patients • 3D SSFP-MOLED provides precise and comprehensive information about tissue characteristics while improving diagnostic accuracy
Keywords:
Quantitative MRI
Multi-parametric mapping
Deep learning
Overlapping-echo detachment
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