Title: aDWI-BIDS: an extension to the brain imaging data structure for advanced diffusion weighted imaging Show Chinese title

Title: aDWI-BIDS：一种用于高级扩散加权成像的脑成像数据结构扩展

Abstract: Diffusion weighted imaging techniques permit us to infer microstructural detail in biological tissue in vivo and noninvasively. Modern sequences are based on advanced diffusion encoding schemes, allowing probing of more revealing measures of tissue microstructure than the standard apparent diffusion coefficient or fractional anisotropy. Though these methods may result in faster or more revealing acquisitions, they generally demand prior knowledge of sequence-specific parameters for which there is no accepted sharing standard. Here, we present a metadata labelling scheme suitable for the needs of developers and users within the diffusion neuroimaging community alike: a lightweight, unambiguous parametric map relaying acqusition parameters. This extensible scheme supports a wide spectrum of diffusion encoding methods, from single diffusion encoding to highly complex sequences involving arbitrary gradient waveforms. Built under the brain imaging data structure (BIDS), it allows storage of advanced diffusion MRI data comprehensively alongside any other neuroimaging information, facilitating processing pipelines and multimodal analyses. We illustrate the usefulness of this BIDS-extension with a range of example data, and discuss the extension's impact on pre- and post-processing software. Show Chinese abstract

Abstract: 扩散加权成像技术使我们能够在体内非侵入性地推断生物组织的微结构细节。 现代序列基于先进的扩散编码方案，能够探测比标准表观扩散系数或部分各向异性更具揭示性的组织微结构测量值。 尽管这些方法可能带来更快或更具有揭示性的采集结果，但它们通常需要特定序列的先验知识，而目前尚无公认的共享标准。 在此，我们提出一种元数据标记方案，适用于扩散神经成像社区中开发人员和用户的需求：一种轻量级、无歧义的参数图，用于传递采集参数。 此可扩展方案支持从单次扩散编码到涉及任意梯度波形的复杂序列的广泛扩散编码方法。 在脑成像数据结构（BIDS）下构建，它允许将高级扩散磁共振成像数据与任何其他神经成像信息全面存储在一起，促进处理流程和多模态分析。 我们通过一系列示例数据展示了这一BIDS扩展的实用性，并讨论了该扩展对预处理和后处理软件的影响。