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.

弥散加权 MRI 使得量化亚体素脑微观结构和重建白质纤维轨迹成为可能，通过该轨迹可以创建结构连接体。然而，在脑脊液和白质之间的边界处，或者存在水肿的情况下，获得的MRI信号既来自脑脊液又来自白质部分体积。扩散纤维束成像会受到这些自由水部分体积效应的强烈影响。因此，包含自由水模型可以改善神经胶质瘤患者的扩散纤维束成像。在这里，我们分析了自由水模型如何影响健康受试者以及脑肿瘤患者的结构连接估计。在一项临床研究中，我们获取了 35 名神经胶质瘤患者和 28 名年龄和性别匹配的对照者的扩散 MRI 数据，并在这些数据上应用了基于开源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性纤维束成像，并利用纤维束图创建结构连接组。最后，我们对连接矩阵进行了定量分析。在我们的实验中，高级别神经胶质瘤患者的跟踪扩散流线数量增加了 13%，低级别神经胶质瘤患者增加了 9.25%，健康对照者增加了 7.65%。患者和对照组的受试者体内半球相似性显着增加，在患者组中观察到更大的影响。此外，当纳入自由水模型时，脑肿瘤患者和健康受试者之间连接性的受试者间差异减少了。 我们的结果表明，自由水模型增加了脑肿瘤患者连接矩阵的相似性，而在健康受试者中观察到的效果不太明显。由于脑肿瘤患者与健康对照之间的相似性也增加，因此在未进行自由水建模的研究中，脑肿瘤患者的连接变化可能被高估了。