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.

中文翻译：

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Dmipy 工具箱：扩散 MRI 多室建模和微观结构恢复变得容易

在过去的几十年里，使用扩散 MRI (dMRI)（称为微结构成像）对大脑微观结构特征进行非侵入性估计已成为一个日益多样化和复杂的领域。多隔室 (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，已以多种形式开发以估计这些特征。然而，作为一个整体的 MC 建模的通用实现，提供对其功能的更深入的见解，仍然缺失。为了解决这个问题，我们介绍了 Python 中的 Diffusion Microstructure Imaging (Dmipy)，这是一个开源工具箱，以最通用的形式实现了基于 PGSE 的 MC 建模。对于任何 PGSE 采集方案，Dmipy 允许对任何用户定义的 MC 模型进行即时实施、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，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、方向、体积分数、轴突方向分散和轴突直径分布。特别是，Dmipy 致力于促进可重复的、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，我们实现了广泛的知名 MC 模型，包括 IVIM、AxCaliber、NODDI(x)、Bingham-NODDI、基于球形均值的 SMT 和 MC-MDI，以及球形卷积基于单组织和多组织的 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现，例如具有体素变化内核的 CSD 和单壳 3 组织 CSD。通过提供经过良好测试、用户友好的工具箱，简化与基于 dMRI 的微结构成像的其他复杂领域的交互，Dmipy 有助于提高可重复性、高质量的研究。