Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy.
Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
Magn Reson Med. 2022 Nov;88(5):2101-2116. doi: 10.1002/mrm.29365. Epub 2022 Jun 29.
To compare different multi-echo combination methods for MRI QSM. Given the current lack of consensus, we aimed to elucidate how to optimally combine multi-echo gradient-recalled echo signal phase information, either before or after applying Laplacian-base methods (LBMs) for phase unwrapping or background field removal.
Multi-echo gradient-recalled echo data were simulated in a numerical head phantom, and multi-echo gradient-recalled echo images were acquired at 3 Tesla in 10 healthy volunteers. To enable image-based estimation of gradient-recalled echo signal noise, 5 volunteers were scanned twice in the same session without repositioning. Five QSM processing pipelines were designed: 1 applied nonlinear phase fitting over TEs before LBMs; 2 applied LBMs to the TE-dependent phase and then combined multiple TEs via either TE-weighted or SNR-weighted averaging; and 2 calculated TE-dependent susceptibility maps via either multi-step or single-step QSM and then combined multiple TEs via magnitude-weighted averaging. Results from different pipelines were compared using visual inspection; summary statistics of susceptibility in deep gray matter, white matter, and venous regions; phase noise maps (error propagation theory); and, in the healthy volunteers, regional fixed bias analysis (Bland-Altman) and regional differences between the means (nonparametric tests).
Nonlinearly fitting the multi-echo phase over TEs before applying LBMs provided the highest regional accuracy of and the lowest phase noise propagation compared to averaging the LBM-processed TE-dependent phase. This result was especially pertinent in high-susceptibility venous regions.
For multi-echo QSM, we recommend combining the signal phase by nonlinear fitting before applying LBMs.
比较 MRI QSM 的不同多回波组合方法。鉴于目前缺乏共识，我们旨在阐明如何在应用拉普拉斯基底方法（LBMs）进行相位解缠或背景场去除之前或之后，最优地组合多回波梯度回波信号相位信息。
在数值头部体模中模拟多回波梯度回波数据，并在 3T 下采集 10 名健康志愿者的多回波梯度回波图像。为了能够基于图像估计梯度回波信号噪声，5 名志愿者在同一次扫描中两次扫描而无需重新定位。设计了 5 个 QSM 处理管道：1 在 LBM 之前对 TE 进行非线性相位拟合；2 将 LBM 应用于 TE 相关相位，然后通过 TE 加权或 SNR 加权平均组合多个 TE；2 通过多步或单步 QSM 计算 TE 相关磁化率图，然后通过幅度加权平均组合多个 TE。使用视觉检查、深灰质、白质和静脉区域磁化率的摘要统计数据、相位噪声图（误差传播理论）以及在健康志愿者中，区域固定偏差分析（Bland-Altman）和区域均值差异（非参数检验）比较不同管道的结果。
与平均 LBM 处理后的 TE 相关相位相比，在应用 LBM 之前对多回波相位进行非线性拟合提供了最高的区域准确性和最低的相位噪声传播。这一结果在高磁化率静脉区域尤其重要。
对于多回波 QSM，我们建议在应用 LBM 之前通过非线性拟合来组合信号相位。
