On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge
On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge
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关于跨采集参数、序列和组织类型的扩散 MRI 信号表示的普遍性：MMENTO 挑战的编年史
DOI：
10.1101/2021.03.02.433228
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
De Luca A
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作者：
De Luca A
扩散 MRI (dMRI) 已成为评估脑组织微观结构的宝贵工具。根据特定的采集设置，dMRI 信号对底层扩散过程的特定属性进行编码。在过去的二十年中，已经提出了几种信号表示来拟合 dMRI 信号并解码此类属性。然而，大多数方法都是在有限数量的数据上进行测试和开发的，并且它们对其他采集方案的适用性仍然未知。通过这项工作，我们的目的是阐明现有 dMRI 信号表示对不同扩散编码参数和脑组织类型的普遍性。为此，我们组织了一个名为 MMENTO 的社区挑战赛，提供相同的数据集以进行算法和技术之间的公平比较。我们考虑了两个最先进的扩散数据集，包括来自人脑的单扩散编码 (SDE) 自旋回波数据，具有超过 3820 个独特的扩散权重（MASSIVE 数据集），以及来自小鼠大脑的双（振荡）扩散编码数据 (DDE/DODE)，包括超过 2520 个独特的数据点。在 5 个不同体素中采样的数据子集被公开分发，挑战参与者被要求预测数据的其余部分。一年后，八个参赛团队总共提交了 80 个信号匹配。对于每次提交，我们评估了均方误差、预测误差的方差和贝叶斯信息标准。收到的提交内容预测了多壳 SDE 数据 (37%) 或 DODE 数据 (22%)，其次是笛卡尔 SDE 数据 (19%) 和 DDE (18%)。大多数提交的材料都非常好地预测了 SDE 测量的信号，但低和非常强的扩散权重除外。 DDE 和 DODE 数据的预测似乎更具挑战性，可能是因为没有提交的材料明确说明了扩散时间和频率。除了模型的选择之外，拟合程序和超参数的决策在预测性能中发挥着重要作用，突出了优化和报告此类选择的重要性。这项工作是社区的努力，旨在强调该领域在代表通过趋势编码方案获得的 dMRI 方面的优势和局限性，深入了解不同模型如何在大范围的扩散编码中推广到不同的组织类型和纤维配置。
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.