Robust frequency-dependent diffusional kurtosis computation using an efficient direction scheme, axisymmetric modelling, and spatial regularization

J. Hamilton, K. Xu, N. Geremia, Vania F Prado, M. A. Prado, A. Brown, Corey Baron
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Abstract

Abstract Frequency-dependent diffusion MRI (dMRI) using oscillating gradient encoding and diffusional kurtosis imaging (DKI) techniques have been shown to provide additional insight into tissue microstructure compared to conventional dMRI. However, a technical challenge when combining these techniques is that the generation of the large b-values (≥2000 s/mm2) required for DKI is difficult when using oscillating gradient diffusion encoding. While efficient encoding schemes can enable larger b-values by maximizing multiple gradient channels simultaneously, they do not have sufficient directions to enable the estimation of directional kurtosis parameters. Accordingly, we investigate a DKI fitting algorithm that combines axisymmetric DKI fitting, a prior that enforces the same axis of symmetry for all oscillating gradient frequencies, and spatial regularization, which together enable robust DKI fitting for a 10-direction scheme that offers double the b-value compared to traditional encoding schemes. Using data from mice (oscillating frequencies of 0, 60, and 120 Hz) and humans (0 Hz only), we first show that axisymmetric DKI fitting provides comparable or even slightly improved image quality as compared to kurtosis tensor fitting, and improved DKI map quality when using an efficient encoding scheme with averaging as compared to a traditional scheme with more encoding directions. We also demonstrate that enforcing consistent axes of symmetries across frequencies improves fitting quality, and spatial regularization during fitting preserves spatial features better than using Gaussian filtering prior to fitting, which is an oft-reported pre-processing step for DKI. Thus, the use of an efficient 10-direction scheme combined with the proposed DKI fitting algorithm provides robust maps of frequency-dependent directional kurtosis which may offer increased sensitivity to cytoarchitectural changes that occur at various cellular spatial scales over the course of healthy aging, and due to pathological alterations.
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利用高效的方向方案、轴对称建模和空间正则化,进行稳健的频率相关扩散峰度计算
摘要 采用振荡梯度编码和弥散峰度成像(DKI)技术的频率依赖性弥散磁共振成像(dMRI)已被证明比传统的 dMRI 更能深入了解组织的微观结构。然而,将这些技术结合起来的一个技术难题是,使用振荡梯度扩散编码时很难生成 DKI 所需的大 b 值(≥ 2000 s/mm2)。虽然高效的编码方案可以通过同时最大化多个梯度通道来获得更大的 b 值,但它们没有足够的方向性来估算方向性峰度参数。因此,我们研究了一种 DKI 拟合算法,该算法结合了轴对称 DKI 拟合、对所有振荡梯度频率强制执行同一对称轴的先验和空间正则化,共同实现了 10 个方向方案的稳健 DKI 拟合,与传统编码方案相比,该方案可提供双倍的 b 值。通过使用小鼠(振荡频率为 0、60 和 120 Hz)和人类(仅 0 Hz)的数据,我们首先证明了轴对称 DKI 拟合与峰度张量拟合相比,可提供相当甚至略有改善的图像质量,而且与具有更多编码方向的传统方案相比,使用具有平均化功能的高效编码方案可改善 DKI 地图质量。我们还证明,在不同频率之间强制使用一致的对称轴可以提高拟合质量,在拟合过程中进行空间正则化比在拟合前使用高斯滤波能更好地保留空间特征,而高斯滤波是经常被报道的 DKI 预处理步骤。因此,使用高效的 10 个方向方案与所提出的 DKI 拟合算法相结合,可提供稳健的频率相关方向峰度图,从而提高对细胞结构变化的敏感性,这些变化发生在健康衰老过程中的各种细胞空间尺度上,也可能是病理改变所致。
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