Dipy, a library for the analysis of diffusion MRI data.
Dipy, a library for the analysis of diffusion MRI data.
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DOI：
10.3389/fninf.2014.00008
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发表时间：
2014
影响因子：
3.5
通讯作者：
Dipy Contributors
中科院分区：
文献类型：
--
作者：
Garyfallidis E;Brett M;Amirbekian B;Rokem A;van der Walt S;Descoteaux M;Nimmo-Smith I;Dipy Contributors
Diffusion Imaging in Python（Dipy）是一个免费的开源软件项目，用于分析扩散磁共振成像（dMRI）实验的数据。dMRI是MRI的一种应用，可用于测量脑白色物质的结构特征。已经开发了许多方法来使用dMRI数据来对白色物质神经纤维束的局部配置进行建模，并推断连接大脑不同部分的束的轨迹。Dipy收集了dMRI中许多不同方法的实现，包括：扩散信号预处理;单个体素中扩散分布的重建;纤维追踪和纤维追踪后处理，分析和可视化。Dipy旨在通过统一的编程接口为dMRI分析的所有不同步骤提供透明的实现。我们已经实现了经典的信号重建技术，如扩散张量模型和确定性纤维束成像。此外，实现了最前沿的新重建技术，如约束球面反卷积和具有反卷积的扩散谱成像（DSI），以及用于概率跟踪的方法和用于纤维束成像聚类的原始方法。提供了许多附加的实用程序函数来计算各种统计数据、信息可视化以及文件处理例程，以帮助开发和使用新技术。与许多其他科学软件项目不同，Dipy不是由一个研究小组开发的。相反，它是一个开放的项目，鼓励任何科学家/开发人员通过GitHub做出贡献，并在项目邮件列表上进行公开讨论。因此，Dipy今天拥有一个国际贡献者团队，跨越五个国家和三大洲的七个不同的学术机构，这一团队仍在不断壮大。
Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.
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影响因子：
3.3
作者：
Chang, LC;Jones, DK;Pierpaoli, C
通讯作者：
Pierpaoli, C
影响因子：
3.3
作者：
Anderson, AW
通讯作者：
Anderson, AW
影响因子：
5.7
作者：
Bermnan, Jeffrey I.;Chung, SungWon;Henry, Roland G.
通讯作者：
Henry, Roland G.
影响因子：
3.7
作者：
Daducci A;Gerhard S;Griffa A;Lemkaddem A;Cammoun L;Gigandet X;Meuli R;Hagmann P;Thiran JP
通讯作者：
Thiran JP
影响因子：
10.9
作者：
Cote, Marc-Alexandre;Girard, Gabriel;Descoteaux, Maxime
通讯作者：
Descoteaux, Maxime