Magnetic resonance in medicine · 2026
Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification.
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Xiong Zhuang, Gao Yang, Liu Feng, Emery Derek, Butcher Ken, Wilman Alan H, Sun Hongfu
Аннотация
PURPOSE: To develop a generative diffusion model-based approach for robust and efficient quantitative susceptibility mapping (QSM) reconstruction in intracranial hemorrhage (ICH), applicable to both standard gradient echo (GRE) and rapid echo planar imaging (EPI) acquisitions.
METHODS: QSMDiff, an unsupervised diffusion model for 3D QSM dipole inversion, was proposed. Three volumetric partitioning strategies including 2D slices, 3D patches, and 2.5D slabs were evaluated, and the memory-efficient 2.5D slab approach was adopted to balance accuracy and efficiency while maintaining anatomical fidelity. A conditional sampling mechanism ensured consistency with measured local fields, and a three-stage training data-generation strategy combining public dataset, synthetic QSM, and ICH lesions simulated from in vivo patients was implemented to overcome data scarcity.
RESULTS: QSMDiff achieved the best overall performance in simulation studies, with SSIM of 0.97 ± 0.07, RMSE of 0.04 ± 0.03, and HFEN of 4.49 ± 0.83, demonstrating superior structural fidelity and noise suppression. For in vivo ICH patients scanned with rapid EPI, QSMDiff showed strong agreement with SWI-QSM references (R2 = 0.83), producing susceptibility estimates with minimal bias and variance. Qualitative evaluation confirmed enhanced resolution and effective artifact suppression in conditions of low SNR, limited resolution, and motion.
CONCLUSION: QSMDiff achieves high-quality and accurate QSM reconstruction from both standard GRE and rapid EPI scans for ICH assessment. By integrating a 2.5D training strategy with synthetic ICH augmentation, it delivers accurate and reliable susceptibility maps even from lower-quality acquisitions, offering a practical solution for fast and robust ICH assessment.
METHODS: QSMDiff, an unsupervised diffusion model for 3D QSM dipole inversion, was proposed. Three volumetric partitioning strategies including 2D slices, 3D patches, and 2.5D slabs were evaluated, and the memory-efficient 2.5D slab approach was adopted to balance accuracy and efficiency while maintaining anatomical fidelity. A conditional sampling mechanism ensured consistency with measured local fields, and a three-stage training data-generation strategy combining public dataset, synthetic QSM, and ICH lesions simulated from in vivo patients was implemented to overcome data scarcity.
RESULTS: QSMDiff achieved the best overall performance in simulation studies, with SSIM of 0.97 ± 0.07, RMSE of 0.04 ± 0.03, and HFEN of 4.49 ± 0.83, demonstrating superior structural fidelity and noise suppression. For in vivo ICH patients scanned with rapid EPI, QSMDiff showed strong agreement with SWI-QSM references (R2 = 0.83), producing susceptibility estimates with minimal bias and variance. Qualitative evaluation confirmed enhanced resolution and effective artifact suppression in conditions of low SNR, limited resolution, and motion.
CONCLUSION: QSMDiff achieves high-quality and accurate QSM reconstruction from both standard GRE and rapid EPI scans for ICH assessment. By integrating a 2.5D training strategy with synthetic ICH augmentation, it delivers accurate and reliable susceptibility maps even from lower-quality acquisitions, offering a practical solution for fast and robust ICH assessment.