RobustT_1ρ,T₂, andT₂^*mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction

Objective.To address the challenges of rapid and robust quantitative MRI, particularly forT_1ρmapping, by developing and evaluating a novel technique-spin-locked (SL) multiple overlapping echo detachment (SL-MOLED)-for efficient mapping ofT_1ρ,T₂, andT₂^*relaxation times with reduced sensitivity toB₀/B₁inhomogeneities and SL -related banding artifacts.Approach.SL-MOLED integrates SL preparation into the MOLED acquisition framework, enabling simultaneous mapping ofT_1ρ,T₂,T₂^*, proton density, and estimation of ΔB₀andB₁in approximately 11 s 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. Short-term within-session stability and 7 d inter-session test-retest repeatability were evaluated separately using ROI-based coefficients of variations.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 forT_1ρ,T₂, andT₂^*compared with models trained without artifact modeling, across varyingB₀/B₁inhomogeneities and SL frequencies. Phantom studies demonstrated good agreement between SL-MOLED reconstructed maps and reference methods forT_1ρ,T₂, andT₂^*(r> 0.997, Bland-Altman bias < 3.4%).In vivoexperiments confirmed strong correlations with reference maps (r⩾ 0.976) and good repeatability. In a patient with multiple sclerosis, SL-MOLED detected elevatedT_1ρvalues in lesions relative to normal-appearing white matter, whileT₂andT₂^*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.