IEEE Trans Med Imaging. 2021 Dec;40(12):3832-3842. doi: 10.1109/TMI.2021.3100293. Epub 2021 Nov 30.
In MR Fingerprinting (MRF), balanced Steady-State Free Precession (bSSFP) has advantages over unbalanced SSFP because it retains the spin history achieving a higher signal-to-noise ratio (SNR) and scan efficiency. However, bSSFP-MRF is not frequently used because it is sensitive to off-resonance, producing artifacts and blurring, and affecting the parametric map quality. Here we propose a novel Spatial Off-resonance Correction (SOC) approach for reducing these artifacts in bSSFP-MRF with spiral trajectories. SOC-MRF uses each pixel's Point Spread Function to create system matrices that encode both off-resonance and gridding effects. We iteratively compute the inverse of these matrices to reduce the artifacts. We evaluated the proposed method using brain simulations and actual MRF acquisitions of a standardized T1/T2 phantom and five healthy subjects. The results show that the off-resonance distortions in T1/T2 maps were considerably reduced using SOC-MRF. For T2, the Normalized Root Mean Square Error (NRMSE) was reduced from 17.3 to 8.3% (simulations) and from 35.1 to 14.9% (phantom). For T1, the NRMS was reduced from 14.7 to 7.7% (simulations) and from 17.7 to 6.7% (phantom). For in-vivo, the mean and standard deviation in different ROI in white and gray matter were significantly improved. For example, SOC-MRF estimated an average T2 for white matter of 77ms (the ground truth was 74ms) versus 50 ms of MRF. For the same example the standard deviation was reduced from 18 ms to 6ms. The corrections achieved with the proposed SOC-MRF may expand the potential applications of bSSFP-MRF, taking advantage of its better SNR property.
在磁共振指纹成像(MRF)中,平衡稳态自由进动(bSSFP)比非平衡稳态自由进动(SSFP)具有优势,因为它保留了自旋历史,从而实现了更高的信噪比(SNR)和扫描效率。然而,bSSFP-MRF 并不常用,因为它对离频非常敏感,会产生伪影和模糊,影响参数图的质量。在这里,我们提出了一种新的空间离频校正(SOC)方法,用于减少螺旋轨迹 bSSFP-MRF 中的这些伪影。SOC-MRF 使用每个像素的点扩散函数来创建系统矩阵,这些矩阵编码离频和栅格效应。我们迭代地计算这些矩阵的逆来减少伪影。我们使用大脑模拟和标准化 T1/T2 体模以及五名健康受试者的实际 MRF 采集来评估所提出的方法。结果表明,SOC-MRF 可显著减少 T1/T2 图中的离频失真。对于 T2,归一化均方根误差(NRMSE)从模拟的 17.3%降低到 8.3%,从体模的 35.1%降低到 14.9%。对于 T1,NRMS 从模拟的 14.7%降低到 7.7%,从体模的 17.7%降低到 6.7%。对于体内,白质和灰质中不同 ROI 的平均值和标准差均得到显著改善。例如,SOC-MRF 估计白质的平均 T2 为 77ms(真实值为 74ms),而 MRF 为 50ms。对于同一示例,标准偏差从 18ms 降低到 6ms。所提出的 SOC-MRF 可以实现的校正可能会扩大 bSSFP-MRF 的潜在应用,利用其更好的 SNR 特性。