Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, United States.
Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham, Alabama, United States.
NMR Biomed. 2021 Jan;34(1):e4414. doi: 10.1002/nbm.4414. Epub 2020 Oct 4.
Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of coexisting inflammation, edema and crossing nerve fibers. We employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Furthermore, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q-sampling imaging (GQI), q-ball imaging (QBI) and neurite orientation dispersion and density imaging to resolve fiber crossing, in reference to the gold standard angles measured from structural images. DTI-computed diffusivities and fractional anisotropy were significantly confounded by gel-mimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionally, DBSI accurately and consistently estimated crossing angles in various conditions of gel-mimicked edema when compared with the gold standard (r = 0.92, P = 1.9 × 10 , bias = 3.9°). Small crossing angles and edema significantly impact the diffusion orientation distribution function, making DKI, GQI and QBI less accurate in detecting and estimating fiber crossing angles. Lastly, we used diffusion tensor ellipsoids to demonstrate that DBSI resolves the confounds of edema and crossing fibers in the peritumoral edema region from a patient with lung cancer metastasis, while DTI failed. In summary, DBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel-mimicked edema. DBSI also indicated better angular resolution compared with DKI, QBI and GQI.
弥散张量成像（DTI）已经被应用了二十多年，用于无创地量化中枢神经系统疾病/损伤。然而，在存在共存的炎症、水肿和交叉神经纤维的情况下，DTI 是对扩散建模的不充分简化。我们使用一种组织模型，使用固定的小鼠三叉神经，涂有不同量的琼脂糖凝胶，以模拟存在血管源性水肿时的交叉纤维。在模拟水肿程度和纤维交叉程度增加的情况下，比较了 DTI 和扩散基础谱成像（DBSI）得出的扩散测量值。此外，我们评估了 DBSI、扩散峰度成像（DKI）、广义 q 采样成像（GQI）、q 球成像（QBI）和神经丝取向弥散和密度成像分辨纤维交叉的能力，以参考结构图像测量的黄金标准角度。DTI 计算的扩散系数和各向异性分数明显受到凝胶模拟水肿和交叉纤维的影响。相反，DBSI 可以计算出单个纤维的准确扩散系数，无论模拟水肿的程度和纤维交叉角度如何。此外，与黄金标准相比，DBSI 在各种凝胶模拟水肿条件下准确一致地估计了交叉角度（r = 0.92，P = 1.9×10 -4 ，偏差 = 3.9°）。小的交叉角度和水肿显著影响扩散方向分布函数，使得 DKI、GQI 和 QBI 在检测和估计纤维交叉角度方面的准确性降低。最后，我们使用扩散张量椭球体来证明 DBSI 能够解决肺癌转移患者肿瘤周围水肿区域水肿和交叉纤维的混淆，而 DTI 则无法解决。总之，DBSI 能够在具有交叉角度和凝胶模拟水肿程度增加的复杂环境中分离出两条交叉纤维，并准确恢复它们的扩散系数。与 DKI、QBI 和 GQI 相比，DBSI 还具有更好的角度分辨率。
