Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. Data were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise (CNR) across the brain. We compared one- and two-flip angle DW-SSFP data using a tensor model that incorporates the full DW-SSFP Buxton signal, in addition to tractography performed over the cingulum bundle and pre-frontal cortex using a ball & sticks model. The two-flip angle DW-SSFP data produced angular uncertainty and tractography estimates close to the CNR optimal regions in the single-flip angle datasets. The two-flip angle tensor estimates were subsequently fitted using a modified DW-SSFP signal model that incorporates a gamma distribution of diffusivities. This allowed us to generate tensor maps at a single effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments.

中文翻译：

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使用多重翻转角测量来解释 DW-SSFP 中的传输不均匀性和非高斯扩散

扩散加权稳态自由进动 (DW-SSFP) 是一种有效信噪比的扩散成像方法。超高场下改进的信噪比和分辨率推动了其在 7T 下的使用。然而，这些数据往往具有严重的 B1 不均匀性，不仅导致信噪比在空间上变化，而且扩散率估计在空间上变化，从而混淆了数据集之间和数据集内的比较。本研究提出以两个翻转角采集 DW-SSFP 数据，并结合非高斯扩散的显式建模来解决 7T 下的 B1 不均匀性问题。数据是从五个固定的人类死后大脑中获取的，这些大脑具有一对翻转角，共同优化了整个大脑的扩散对比噪声（CNR）。我们使用包含完整 DW-SSFP Buxton 信号的张量模型比较了单翻转角和双翻转角 DW-SSFP 数据，此外还使用球棒模型对扣带束和前额皮质进行了纤维束成像。双翻转角 DW-SSFP 数据产生的角度不确定性和纤维束成像估计接近单翻转角数据集中的 CNR 最佳区域。随后使用修改后的 DW-SSFP 信号模型拟合了两次翻转角张量估计，该模型包含扩散系数的伽马分布。这使我们能够以单个有效 b 值生成张量图，从而在组织中产生更一致的 SNR，此外还消除了 B1 对扩散系数和方向图的依赖。我们提出的方法将允许在 7T 下使用 DW-SSFP 来导出具有更大可解释性的扩散率估计，无论是在单个数据集内还是在实验之间。