提出新方法自动重建脑部磁敏感图，保留更多细节且无需手动调参。
Laplace-Mixture Dipole Inversion for Quantitative Susceptibility Mapping
- 用双成分拉普拉斯混合先验替代单个拉普拉斯先验，更好捕捉图像梯度分布
- 在公开数据集上比现有方法更有效保留高频解剖细节，HFEN显著降低
- 无需参考图或人工调参，适合临床和自动化流程使用
目的：开发一种自动化的磁偶极子反演方法用于定量磁敏感成像（QSM），在无需手动调节正则化参数的情况下保留精细解剖结构。理论：原有基于参数估计的近似消息传递（AMP-PE）框架采用单一拉普拉斯先验建模图像梯度，未能充分反映脑磁敏感图中梯度的重尾分布，导致过度正则化与块状重建。为此，本文采用双成分拉普拉斯混合先验建模梯度分布。方法：将双成分拉普拉斯混合先验引入AMP-PE框架，提出拉普拉斯混合偶极子反演（LAMDI）方法，并实现自动参数估计。在公开体内数据集上评估性能，与FANSI、MEDI及带单拉普拉斯先验的AMP-PE（AMP-PE-L1）在标准默认与参考调优设置下进行对比。结果：在公开多方向QSM数据集上，LAMDI在NRMSE和SSIM方面与AMP-PE-L1相当，同时大幅降低HFEN，表明高频率解剖细节得以更好保留。在参考调优下，FANSI与MEDI在部分指标上表现最佳，但LAMDI仍具竞争力，且无需参考图或人工调参。结论：LAMDI通过结合优异重建精度与更佳细节保持能力，为QSM偶极子反演提供了一种有效且自动的参数估计方案。
原文摘要 · Abstract (English)
Purpose: To develop an automatic dipole inversion method for quantitative susceptibility mapping (QSM) that preserves fine anatomical structures without the need for manual regularization-parameter tuning. Theory: The original approximate message passing with parameter estimation (AMP-PE) framework models image gradients with a single Laplace prior, which does not fully capture the heavy-tailed gradient distribution of brain susceptibility maps. This prior mismatch can lead to over-regularization and blocky reconstructions. We address this limitation by modeling the gradients with a two-component Laplace mixture prior. Methods: We propose a Laplace-Mixture Dipole Inversion (LAMDI) method by incorporating a two-component Laplace mixture prior into the AMP-PE framework with automatic parameter estimation. LAMDI was evaluated on a public in vivo dataset. Its performance was compared with FANSI, MEDI, and AMP-PE with a single-Laplace prior (AMP-PE-L1) under both standard default and reference-tuned settings. Results: On a public multi-orientation QSM dataset, LAMDI achieved NRMSE and SSIM comparable to AMP-PE-L1 while substantially reducing HFEN, suggesting improved preservation of high-frequency anatomical detail. Under reference-based tuning, FANSI and MEDI achieved the best performance for some metrics, but LAMDI remained competitive without requiring reference maps or manual regularization tuning. Conclusion: LAMDI provides an effective and automatic parameter-estimation alternative for QSM dipole inversion by combining competitive reconstruction accuracy with improved preservation of fine anatomical detail.
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