Xiaoming Yin, Andrew C. Larson Department of Radiology, Northwestern University, Chicago, IL, USA.
NMR Biomed. 2010 Dec;23(10):1127-36. doi: 10.1002/nbm.1539.
Accurate R2* measurements are critical for many abdominal imaging applications. Conventionally, R2* maps are derived via the monoexponential fitting of signal decay within a series of gradient-echo (GRE) images reconstructed from multichannel datasets combined using a root sum-of-squares (RSS) approach. However, the noise bias at low-SNR TEs from RSS-reconstructed data often causes the underestimation of R2* values. In phantom, ex vivo animal model and normal volunteer studies, we investigated the accuracy of low-SNR R2* measurement when combining truncation and coil combination methods. The accuracy for R2* estimations was shown to be affected by the intrinsic R2* value, SNR level and the chosen reconstruction method. The R2* estimation error was found to decrease with increasing SNR level, decreasing R2* value and the use of the optimal B1-weighted combined (OBC) image reconstruction method. Data truncation based on rigorous voxel-wise SNR estimates can reduce R2* measurement error in the setting of low SNR with fast signal decay. When optimal SNR truncation thresholds are unknown, the OBC method can provide optimal R2* measurements given the minimal truncation requirements.
准确的 R2* 测量对于许多腹部成像应用至关重要。传统上，通过对来自多通道数据集的一系列梯度回波 (GRE) 图像的信号衰减进行单指数拟合，使用均方根和 (RSS) 方法对其进行组合，从而得出 R2* 图。然而，RSS 重建数据在低 SNR TE 处的噪声偏差常常导致 R2* 值的低估。在体模、离体动物模型和正常志愿者研究中，我们研究了在结合截断和线圈组合方法时低 SNR R2* 测量的准确性。R2* 估计的准确性受到固有 R2* 值、SNR 水平和所选重建方法的影响。发现 R2* 估计误差随 SNR 水平的增加、R2* 值的降低以及使用最佳 B1 加权组合 (OBC) 图像重建方法而减小。基于严格的体素 SNR 估计的基于数据截断可以在低 SNR 快速信号衰减的情况下减少 R2* 测量误差。在最优 SNR 截断阈值未知的情况下，OBC 方法可以在最小截断要求下提供最佳的 R2* 测量值。
