Robust T1ρ , T2 , and T2 * mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction.
Authors
Affiliations (3)
Affiliations (3)
- Department of Electronic Science, Xiamen University, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen, Fujian, 361102, China.
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Xiamen, Fujian, 361004, China.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Charlestown, Massachusetts, 02129, United States.
Abstract
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.
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