Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
[Quantitative susceptibility reconstruction guided by a multi-scale T1-weighted image attention mechanism].
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Li Piaoran, Tao Quan, Oyang Fusheng, Guo Baoliang, Hu Qiugen, Feng Yanqiu
Аннотация
OBJECTIVES: To improve the accuracy and structural consistency of quantitative susceptibility mapping (QSM) reconstruction through a deep learning network guided by multi-scale T1-weighted image attention.
METHODS: A multi-scale T1-weighted attention-driven deep QSM network (T1w-ADQSM) was proposed to enhance the precision and structural fidelity of single-orientation QSM reconstruction. The method incorporates structural features from T1-weighted images and employs an attention module to guide the network to focus on the anatomical boundaries and key regions. Experimental comparisons were conducted among T1w-ADQSM,truncated k-space division (TKD) method, morphology enabled dipole inversion (MEDI) and QSMnet. Quantitative evaluations were performed using the metrics including high-frequency error norm (HFEN), structural similarity index measure (SSIM),normalized root mean square error (NRMSE), and peak signal-to-noise ratio (PSNR).
RESULTS: In healthy volunteers, T1w-ADQSM achieved the highest PSNR (43.12±1.19) and the lowest NRMSE (51.98±3.65) compared with TKD, MEDI, and QSMnet. T1w-ADQSM outperformed QSMnet across all the 4 quantitative metrics with statistically significant differences (P<0.05). Further validation based on the COSMOS reference reconstructed from multi-orientation data showed that T1w-ADQSM exhibited better structural consistency than QSMnet, and the reconstruction results were closer to the COSMOS reference.
CONCLUSIONS: The proposed multi-scale T1-weighted image attention-driven deep learning reconstruction method improves the accuracy and structural consistency of QSM.
METHODS: A multi-scale T1-weighted attention-driven deep QSM network (T1w-ADQSM) was proposed to enhance the precision and structural fidelity of single-orientation QSM reconstruction. The method incorporates structural features from T1-weighted images and employs an attention module to guide the network to focus on the anatomical boundaries and key regions. Experimental comparisons were conducted among T1w-ADQSM,truncated k-space division (TKD) method, morphology enabled dipole inversion (MEDI) and QSMnet. Quantitative evaluations were performed using the metrics including high-frequency error norm (HFEN), structural similarity index measure (SSIM),normalized root mean square error (NRMSE), and peak signal-to-noise ratio (PSNR).
RESULTS: In healthy volunteers, T1w-ADQSM achieved the highest PSNR (43.12±1.19) and the lowest NRMSE (51.98±3.65) compared with TKD, MEDI, and QSMnet. T1w-ADQSM outperformed QSMnet across all the 4 quantitative metrics with statistically significant differences (P<0.05). Further validation based on the COSMOS reference reconstructed from multi-orientation data showed that T1w-ADQSM exhibited better structural consistency than QSMnet, and the reconstruction results were closer to the COSMOS reference.
CONCLUSIONS: The proposed multi-scale T1-weighted image attention-driven deep learning reconstruction method improves the accuracy and structural consistency of QSM.