KU Leuven, Department of Imaging and pathology, Translational MRI, Leuven, Belgium; KU Leuven, Leuven Brain Institute (LBI), Department of Neurosciences, Leuven, Belgium.
KU Leuven, Department of Imaging and pathology, Translational MRI, Leuven, Belgium; KU Leuven, Leuven Brain Institute (LBI), Department of Neurosciences, Leuven, Belgium; UZ Leuven, Department of Radiology, Leuven, Belgium.
Neuroimage. 2022 Jul 1;254:119029. doi: 10.1016/j.neuroimage.2022.119029. Epub 2022 Feb 26.
Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals.
使用弥散磁共振纤维追踪技术对脑白质(WM)进行虚拟解剖存在可重复性差的问题。尽管先进的重建模型已被广泛采用,但基于弥散张量成像(DTI)的早期基于感兴趣区的方法仍然是虚拟解剖方法的主要参考标准。在这里,我们通过提供一种使用基于概率约束球谐反卷积(CSD)纤维追踪的可重复的自动个体特定分割方法重建典型 WM 解剖结构的全面描述来弥补这一差距。我们还补充了一个包含 68 束 WM 模板的 MNI 空间模板,其中包括所有相关的解剖束选择标签和相关的自动工作流程。此外,我们使用来自人类连接组计划(HCP)的 40 个(20 个测试-再测试)数据集和来自单个受试者多采集标准化结构成像验证和评估(MASSIVE)数据集的不同 b 值和 b 壳数量的 5 个会话,展示了束间和束内个体间的可变性。最可靠重建的束是整个锥体束、初级皮质脊髓束、整个上纵束、胼胝体的额、顶和枕部以及小脑脚。在不太密集的束中发现了更多的可变性,例如穹窿、齿状红核丘脑束(DRTT)和运动前锥体束。以 DRTT 为例,我们表明,通过增加种子点的数量可以减少这种可变性。HCP 测试-再测试数据的整体会话内相似性很高(中位数加权骰子为 0.963,标准差为 0.201,IQR 为 0.099)。与 HCP 模板束相比,HCP 测试-再测试数据的一致性很高(中位数加权骰子为 0.747,标准差为 0.220,IQR 为 0.277),而 MASSIVE 数据的一致性也很高(中位数加权骰子为 0.767,标准差为 0.255,IQR 为 0.338)。总之,这个 WM 图谱提供了一个概述,展示了自动化个体特定概率 CSD 纤维追踪在健康成年人中映射白质束的能力和局限性。它将在需要可重复的基于分割的解剖协议的应用中最有用,并作为应用神经影像学和临床专业人员的教育资源。
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