Magnetic resonance in medicine · 2025
Instantaneous Abdominal T2* Mapping via Single-Shot MOLED Under Free-Breathing: A Preliminary Study of Hepatic Glycometabolism Imaging.
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Huang Ping, Dai Chenyang, Yang Qinqin, Zhu Liuhong, Zhou Jianjun, Cai Congbo, Cai Shuhui
Аннотация
PURPOSE: To propose a gradient-echo multiple overlapping-echo detachment (GRE-MOLED) method for rapid abdominal T2* mapping, and to systematically validate its efficacy in non-invasive monitoring of dynamic hepatic glycometabolism.
METHODS: The GRE-MOLED sequence was optimized to achieve ultrafast abdominal T2* mapping under free-breathing without respiratory gating, the scan time per slice was only 60 ms. A deep neural network architecture was developed and trained on synthetic data generated through Bloch equation simulations to achieve direct end-to-end reconstruction of T2* maps from GRE-MOLED images. The proposed method was evaluated through numerical abdomen experiments, water phantom experiments, test-retest experiments in healthy volunteers, and dynamic glycometabolic experiments.
RESULTS: Quantitative analyses revealed excellent agreement between the results of our method and reference method. Numerical abdomen experiment achieved mean structural similarity index (SSIM) of 0.9818 and voxel-wise R2 = 0.9163. Water phantom experiments demonstrated high linear correlation (R2 = 0.9992) with a Bland-Altman bias of 0.2686 ms (95% limits of agreement: -1.1048 to 1.6420 ms). Test-retest analysis showed intra-subject coefficient of variation (CV) below 7% across multiple organs. Dynamic glycometabolism mapping successfully captured postprandial hepatic T2* fluctuations with temporal resolution outperforming conventional breath-holding methods.
CONCLUSION: The GRE-MOLED method enables robust abdominal T2* mapping under free-breathing, demonstrating obvious potential for non-invasive assessment of hepatic glycometabolic dynamics. Validation across numerical simulations, water phantom and in vivo experiments confirm technical reliability and high reproducibility.
METHODS: The GRE-MOLED sequence was optimized to achieve ultrafast abdominal T2* mapping under free-breathing without respiratory gating, the scan time per slice was only 60 ms. A deep neural network architecture was developed and trained on synthetic data generated through Bloch equation simulations to achieve direct end-to-end reconstruction of T2* maps from GRE-MOLED images. The proposed method was evaluated through numerical abdomen experiments, water phantom experiments, test-retest experiments in healthy volunteers, and dynamic glycometabolic experiments.
RESULTS: Quantitative analyses revealed excellent agreement between the results of our method and reference method. Numerical abdomen experiment achieved mean structural similarity index (SSIM) of 0.9818 and voxel-wise R2 = 0.9163. Water phantom experiments demonstrated high linear correlation (R2 = 0.9992) with a Bland-Altman bias of 0.2686 ms (95% limits of agreement: -1.1048 to 1.6420 ms). Test-retest analysis showed intra-subject coefficient of variation (CV) below 7% across multiple organs. Dynamic glycometabolism mapping successfully captured postprandial hepatic T2* fluctuations with temporal resolution outperforming conventional breath-holding methods.
CONCLUSION: The GRE-MOLED method enables robust abdominal T2* mapping under free-breathing, demonstrating obvious potential for non-invasive assessment of hepatic glycometabolic dynamics. Validation across numerical simulations, water phantom and in vivo experiments confirm technical reliability and high reproducibility.