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首页> 外文期刊>PLoS One >Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients
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Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients

机译:胶质瘤患者深度学习对游离水建模的影响

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Diffusion-weighted MRI makes it possible to quantify subvoxel brain microstructure and to reconstruct white matter fiber trajectories with which structural connectomes can be created. However, at the border between cerebrospinal fluid and white matter, or in the presence of edema, the obtained MRI signal originates from both the cerebrospinal fluid as well as from the white matter partial volume. Diffusion tractography can be strongly influenced by these free water partial volume effects. Thus, including a free water model can improve diffusion tractography in glioma patients. Here, we analyze how including a free water model influences structural connectivity estimates in healthy subjects as well as in brain tumor patients. During a clinical study, we acquired diffusion MRI data of 35 glioma patients and 28 age- and sex-matched controls, on which we applied an open-source deep learning based free water model. We performed deterministic as well as probabilistic tractography before and after free water modeling, and utilized the tractograms to create structural connectomes. Finally, we performed a quantitative analysis of the connectivity matrices. In our experiments, the number of tracked diffusion streamlines increased by 13% for high grade glioma patients, 9.25% for low grade glioma, and 7.65% for healthy controls. Intra-subject similarity of hemispheres increased significantly for the patient as well as for the control group, with larger effects observed in the patient group. Furthermore, inter-subject differences in connectivity between brain tumor patients and healthy subjects were reduced when including free water modeling. Our results indicate that free water modeling increases the similarity of connectivity matrices in brain tumor patients, while the observed effects are less pronounced in healthy subjects. As the similarity between brain tumor patients and healthy controls also increased, connectivity changes in brain tumor patients may have been overestimated in studies that did not perform free water modeling.
机译:扩散加权MRI使得可以量化子痫脑微观结构并重建可以创建结构螺栓的白质纤维轨迹。然而,在脑脊液和白质之间的边界处,或在水肿存在下,所获得的MRI信号源自脑脊液以及来自白质的部分体积。扩散牵引可以受到这些自由水部分体积效应的强烈影响。因此,包括自由水模型可以改善胶质瘤患者的扩散牵引。在这里,我们分析了如何在内的自由水模型影响健康受试者以及脑肿瘤患者的结构连接估计。在临床研究期间,我们获得了35例胶质瘤患者的扩散MRI数据和28岁和性别匹配的控制,我们应用了一种基于开放的深度学习的自由水模型。我们在自由水建模之前和之后进行了确定性和概率杂物图,并利用了牵引图来产生结构钢丝。最后,我们对连接矩阵进行了定量分析。在我们的实验中,高级胶质瘤患者的跟踪扩散流的数量增加了13%,低等级胶质瘤的9.25%,健康对照的7.65%。对于患者以及对照组,半球的主体相似性显着增加,患者组中观察到较大的效果。此外,当包括游离水建模时,减少了脑肿瘤患者和健康受试者之间连通性的间歇性差异。我们的结果表明,自由水建模增加了脑肿瘤患者的连通矩阵的相似性,而观察到的效果在健康受试者中不太明显。由于脑肿瘤患者和健康对照之间的相似性也增加,脑肿瘤患者的连接变化可能在没有进行免费水建模的研究中受到过度估计。

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