Groupwise track filtering via iterative message passing and pruning.
Groupwise track filtering via iterative message passing and pruning.
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DOI：
10.1016/j.neuroimage.2020.117147
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发表时间：
2020-11-01
期刊：
影响因子：
5.7
通讯作者：
Shi Y
中科院分区：
文献类型：
--
作者：
Xia Y;Shi Y
纤维束成像是基于扩散 MRI 数据对大脑连接进行体内分析的重要工具，但它在忠实重建神经解剖学方面也存在误报和误报的局限性。即使存在以多个感兴趣区域 (ROI) 形式存在的强大解剖学先验来限制纤维束成像的轨迹，这些问题仍然存在。在这项工作中，我们通过利用跨受试者自然存在的纤维束的分组一致性，提出了一种新颖的轨迹过滤方法。我们首先通过灵活的定义来形式化我们的分组概念，该定义基于三个重要方面：程度、亲和力和接近度来描述轨道相对于其他小组成员的一致性。然后开发迭代算法，通过来自参考集的消息传递来动态更新所有流线的局部一致性度量，然后通知从每个流线中修剪异常点。在我们的实验中，我们成功地将我们的方法应用于阿尔茨海默病神经成像计划（ADNI）和人类连接组计划（HCP）的不同分辨率的扩散成像数据，以一致地重建人脑中的三个重要纤维束：穹窿、蓝斑通路和皮质脊髓束。定性评估和定量比较都表明我们的方法在增强纤维束的解剖保真度方面取得了显着的进步。
Tractography is an important tool for the in vivo analysis of brain connectivity based on diffusion MRI data, but it also has well-known limitations in false positives and negatives for the faithful reconstruction of neuroanatomy. These problems persist even in the presence of strong anatomical priors in the form of multiple region of interests (ROIs) to constrain the trajectories of fiber tractography. In this work, we propose a novel track filtering method by leveraging the groupwise consistency of fiber bundles that naturally exists across subjects. We first formalize our groupwise concept with a flexible definition that characterizes the consistency of a track with respect to other group members based on three important aspects: degree, affinity, and proximity. An iterative algorithm is then developed to dynamically update the localized consistency measure of all streamlines via message passing from a reference set, which then informs the pruning of outlier points from each streamline. In our experiments, we successfully applied our method to diffusion imaging data of varying resolutions from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Human Connectome Project (HCP) for the consistent reconstruction of three important fiber bundles in human brain: the fornix, locus coeruleus pathways, and corticospinal tract. Both qualitative evaluations and quantitative comparisons showed that our method achieved significant improvement in enhancing the anatomical fidelity of fiber bundles.
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影响因子：
3.5
作者：
Garyfallidis E;Brett M;Amirbekian B;Rokem A;van der Walt S;Descoteaux M;Nimmo-Smith I;Dipy Contributors
通讯作者：
Dipy Contributors
DOI：
10.1016/j.jalz.2011.05.2416
发表时间：
2012
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Mielke MM;Okonkwo OC;Oishi K;Mori S;Tighe S;Miller MI;Ceritoglu C;Brown T;Albert M;Lyketsos CG
通讯作者：
Lyketsos CG
影响因子：
29
作者：
Gibbons GS;Lee VMY;Trojanowski JQ
通讯作者：
Trojanowski JQ
影响因子：
5.7
作者：
Garyfallidis, Eleftherios;Ocegueda, Omar;Descoteaux, Maxime
通讯作者：
Descoteaux, Maxime
影响因子：
5.7
作者：
Fonov V;Evans AC;Botteron K;Almli CR;McKinstry RC;Collins DL;Brain Development Cooperative Group
通讯作者：
Brain Development Cooperative Group