imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.
Magn Reson Med. 2018 Aug;80(2):802-813. doi: 10.1002/mrm.27075. Epub 2018 Feb 2.
Diffusion kurtosis imaging (DKI) is an advanced magnetic resonance imaging modality that is known to be sensitive to changes in the underlying microstructure of the brain. Image voxels in diffusion weighted images, however, are typically relatively large making them susceptible to partial volume effects, especially when part of the voxel contains cerebrospinal fluid. In this work, we introduce the "Diffusion Kurtosis Imaging with Free Water Elimination" (DKI-FWE) model that separates the signal contributions of free water and tissue, where the latter is modeled using DKI.
A theoretical study of the DKI-FWE model, including an optimal experiment design and an evaluation of the relative goodness of fit, is carried out. To stabilize the ill-conditioned estimation process, a Bayesian approach with a shrinkage prior (BSP) is proposed. In subsequent steps, the DKI-FWE model and the BSP estimation approach are evaluated in terms of estimation error, both in simulation and real data experiments.
Although it is shown that the DKI-FWE model parameter estimation problem is ill-conditioned, DKI-FWE was found to describe the data significantly better compared to the standard DKI model for a large range of free water fractions. The acquisition protocol was optimized in terms of the maximally attainable precision of the DKI-FWE model parameters. The BSP estimator is shown to provide reliable DKI-FWE model parameter estimates.
The combination of the DKI-FWE model with BSP is shown to be a feasible approach to estimate DKI parameters, while simultaneously eliminating free water partial volume effects. Magn Reson Med 80:802-813, 2018. © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
扩散峰度成像（DKI）是一种先进的磁共振成像方式，已知其对大脑下组织结构的变化敏感。然而，在扩散加权图像中，图像体素通常相对较大，容易受到部分容积效应的影响，尤其是当体素的一部分包含脑脊液时。在这项工作中，我们引入了“具有自由水消除的扩散峰度成像”（DKI-FWE）模型，该模型分离了自由水和组织的信号贡献，其中后者使用 DKI 进行建模。
对 DKI-FWE 模型进行了理论研究，包括最优实验设计和相对拟合优度的评估。为了稳定病态估计过程，提出了一种具有收缩先验（BSP）的贝叶斯方法。在后续步骤中，在模拟和真实数据实验中，根据估计误差评估了 DKI-FWE 模型和 BSP 估计方法。
虽然表明 DKI-FWE 模型参数估计问题是病态的，但与标准 DKI 模型相比，在很大的自由水分数范围内，DKI-FWE 被发现能够更好地描述数据。根据 DKI-FWE 模型参数的最大可达精度优化了采集方案。BSP 估计器被证明能够提供可靠的 DKI-FWE 模型参数估计。
将 DKI-FWE 模型与 BSP 相结合被证明是一种可行的方法，可以估计 DKI 参数，同时消除自由水部分容积效应。磁共振医学 80：802-813，2018。© 2018 作者磁共振医学由 Wiley 期刊出版公司代表国际磁共振学会出版。这是在知识共享署名非商业许可下的条款下允许使用、分发和复制的原创作品，只要原始作品被正确引用并且不用于商业目的。
