论文标题
通过近似消息传递和参数估计，可靠的定量敏感性映射
Robust Quantitative Susceptibility Mapping via Approximate Message Passing with Parameter Estimation
论文作者
论文摘要
目的：对于定量敏感性映射（QSM），在临床环境中缺乏地面真相使得确定偶极反转的合适参数是具有挑战性的。我们提出了一种具有内置参数估计的QSM的概率贝叶斯方法，并结合了偶极反转的非线性公式，以实现易感性图的稳健恢复。 理论：从贝叶斯的角度来看，图像小波系数近似稀疏，并由拉普拉斯分布建模。测量噪声是通过具有两个组件的高斯混合分布来建模的，其中第二个组件用于对噪声异常值进行建模。通过概率推断，可以使用近似消息传递（AMP）共同恢复易感性图和分布参数。 方法：我们将所提出的AMP与内置参数估计（AMP-PE）与模拟和体内数据集上的最新的L1-QSM，Fansi和Medi方法进行比较，并执行实验以探索AMP-PE的最佳设置。可重复的代码可在https://github.com/emorycn2l/qsm_amp_pe上找到 结果：在模拟的SIM2SNR1数据集上，AMP-PE获得了最低的NRMSE，DFCM和最高的SSIM，而MEDI获得了最低的HFEN。在体内数据集上，AMP-PE具有强大的功能，并使用估计的参数成功地恢复了易感性图，而L1-QSM，FANSI和MEDI通常需要其他视觉微调来选择或双重检查工作参数。 结论：AMP-PE为QSM提供了自动和自适应参数估计，并避免了视觉微调步骤的主观性，这使其成为临床环境的绝佳选择。
Purpose: For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps. Theory: From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modelled by the Laplace distribution. The measurement noise is modelled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP). Methods: We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at https://github.com/EmoryCN2L/QSM_AMP_PE Results: On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, DFCM and the highest SSIM, while MEDI achieved the lowest HFEN. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters. Conclusion: AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.
