Robust T1ρ, T2, and T2* mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction
Objective. To address the challenges of rapid and robust quantitative MRI, particularly for T1ρ mapping, by developing and evaluating a novel technique—spin-locked (SL) multiple overlapping echo detachment (SL-MOLED)—for efficient mapping of T1ρ, T2, and T2* relaxation times with reduced sensitivity to B0/B1 inhomogeneities and SL –related banding artifacts. Approach. SL-MOLED integrates SL preparation into the MOLED acquisition framework, enabling simultaneous mapping of T1ρ, T2, T2*, proton density, and estimation of ΔB0 and B1 in 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 for T1ρ, T2, and T2* compared with models trained without artifact modeling, across varying B0/B1 inhomogeneities and SL 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. 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.