Abstract

Quantitative magnetic resonance imaging (qMRI) offers reliable biomarkers in clinic. Nevertheless, most qMRI methods are time-consuming and sensitive to motion. Single-shot multiple overlapping-echo detachment (MOLED) magnetic resonance imaging can deliver robust Tmapping in about 100 ms with high motion tolerance. However, its spatial resolution is relatively low due to the limitations of signal-to-noise ratio (SNR) and echo-train length. At the mean time, the number of echoes with different evolution times collected is usually limited, which is not conducive to Tmapping in high accuracy. To propose a novel method to improve the spatial resolution and quantification accuracy of single-shot MOLED Tmapping. A new method called switching modulation patterns multiple overlapping-echo detachment imaging (SWP-MOLED) was designed for multi-slice information sharing via switching the k-space modulation pattern of MOLED imaging. In the SWP-MOLED pulse sequence, three different k-space modulation patterns were devised, making the 12 main echoes of any three adjacent slices symmetrically and uniformly distributed around their k-space centers to obtain diverse contrast weighting information. A multi-slice fusion three-dimensional spatial attention context-guided U-Net was trained with 3000/7000 synthetic data with geometric/brain patterns to efficiently learn the mapping relationship between SWP-MOLED signals and Tmaps. Experiments on numerical human brains, a phantom containing MnClsolutions with different concentrations, three healthy volunteers, and three patients diagnosed with meningioma or glioblastoma were performed. The effectiveness of the new method was quantitatively assessed using the structure similarity index measure (SSIM) and root mean square error (RMSE). Multiple statistical analyses were utilized to evaluate the accuracy and significance of the method, including linear regression, Bland-Altman analysis, Mann-Whitney test, Wilcoxon signed rank test, and Friedman test with Bonferroni correction, with the p-value significance level of 0.05. The results from numerical human brain (The average SSIM of the reconstructed Tmaps was 0.9742/0.9782/0.9826 for MOLED/MS-MOLED/SWP-MOLED) and phantom (The slope of linear fitting of the predicted Tvalues vs. reference values was 0.9934/9942/0.9972 for MOLED/MS-MOLED/SWP-MOLED) demonstrated that more accurate Tmaps were delivered by the proposed method, closely resembling the reference maps. From the Friedman test performed on the results of the test data set after the multi-comparison correction, we found that the pairwise performance differences among different reconstruction networks were all statistically significant (p < 0.001). In healthy human brain experiments, the comparison of SWP-MOLED reconstruction with reference measurements indicated no significant difference (p = 0.4504). SWP-MOLED was quite repeatable (average coefficient of variation [CV] = 4.17%) and was not corrupted by motion (average CV = 7.49%). Moreover, the proposed method exhibited clearer lesion contours in clinical cases, demonstrating the potential of the proposed method for clinical applications. SWP-MOLED can efficiently exploit the structural similarity and parameter-weighted information diversity of adjacent slices to improve the spatial resolution and quantification accuracy of MOLED Tmapping. It also exhibits excellent motion robustness. This technique would extend the application of MOLED imaging.