Robust T1ρ , T2 , and T2 * mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction.

To address the challenges of rapid and robust quantitative MRI, particularly for T1ρ mapping, by developing and evaluating a novel technique-spin-locked multiple overlapping echo detachment (SL-MOLED)-for efficient mapping of T1ρ, T2, and T2* relaxation times with reduced sensitivity to B0/B1 inhomogeneities and spin-lock-related banding artifacts. Approach: SL-MOLED integrates spin-lock preparation into the MOLED acquisition framework, enabling simultaneous mapping of T1ρ, T2, T2*, proton density, and estimation of ΔB0 and B1 in approximately 11 seconds per slice. A synthetic data-driven deep learning reconstruction framework was trained on Bloch-simulated datasets with explicitly modeled banding artifacts, allowing effective mitigation of artifact-related errors. Validation comprised numerical experiments, phantom studies, healthy volunteer experiments, and a preliminary patient evaluation. Reconstruction accuracy was assessed using the structural similarity index measure (SSIM), mean absolute error (MAE), Pearson's correlation coefficient (r), and Bland-Altman analysis, whereas repeatability was evaluated using the coefficient of variation (CV). Main results: Numerical experiments showed that networks trained with artifact modeling improved SSIM by 0.1-0.2 and reduced MAE by 2-5 ms for T1ρ, T2, and T2* compared with models trained without artifact modeling, across varying B0/B1 inhomogeneities and spin-lock frequencies. Phantom studies demonstrated good agreement between SL-MOLED reconstructed maps and reference methods for T1ρ, T2, and T2* (r > 0.997, Bland-Altman bias < 3.4%). In vivo experiments confirmed strong correlations with reference maps (r ≥ 0.976) and good repeatability (CV < 3.3%). In a patient with multiple sclerosis, SL-MOLED detected elevated T1ρ values in lesions relative to normal-appearing white matter, while T2 and T2* showed smaller changes, indicating that each parameter may reflect different underlying pathological features. Significance: SL-MOLED provides accurate, repeatable, and artifact-robust quantitative mapping within a substantially reduced acquisition time (~11 s per slice), offering a promising framework for reliable multi-parametric MRI with potential for clinical translation.&#xD.