The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy
The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy
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DOI：
10.3389/fninf.2019.00064
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发表时间：
2019-10-15
影响因子：
3.5
通讯作者：
Deriche, Rachid
中科院分区：
文献类型：
--
作者：
Fick, Rutger H. J.;Wassermann, Demian;Deriche, Rachid
在过去的几十年里，使用扩散磁共振成像(DMRI)对脑微结构特征的非侵入性估计，即微结构成像，已经成为一个日益多样化和复杂的领域。多间隔(MC)模型，将测量的扩散信号表示为不同组织类型的信号模型的线性组合，已经以多种形式被开发来估计这些特征。然而，作为一个整体的MC建模的通用实现，提供了对其功能的更深层次的洞察，仍然缺乏。为了解决这一问题，我们提出了在Python中的扩散微结构成像(Dmipy)，这是一个开源工具箱，以其最通用的形式实现了基于PGSE的MC建模。Dmipy允许对任何PGSE采集方案的任何用户定义的MC模型进行动态实施、信号建模和优化。Dmipy遵循基于“积木”的微结构成像理念，这意味着MC-模型是模块化构建的，可以包括任何数量和类型的组织模型，允许同时表示组织的扩散率、取向、体积分数、轴突取向散布和轴突直径分布。特别是，Dmipy致力于促进可重现的、可靠的MC建模管道，通常允许在不到10行代码中完成从模型构建到参数映射恢复的整个过程。为了展示Dmipy的易用性和潜力，我们实现了一系列著名的MC模型，包括IVIM、AxCaliber、Node di(X)、Bingham-Node di、基于球面平均的SMT和MC-MDI，以及基于球面卷积的单组织和多组织CSD。通过允许MC模型之间的参数级联，Dmipy还促进了高级方法的实施，如使用体素可变内核的CSD和单壳3组织CSD。通过提供一个经过良好测试的、用户友好的工具箱，简化了与基于dMRI的显微结构成像领域的交互，Dmipy为更可重复性、高质量的研究做出了贡献。
Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)- known as Microstructure Imaging-has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a "building block"-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single- and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.