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.
复制标题
DOI：
10.1016/j.neuroimage.2021.118367
复制
发表时间：
2021-10-15
期刊：
影响因子：
5.7
通讯作者：
Schilling KG
中科院分区：
文献类型：
--
作者：
De Luca A;Ianus A;Leemans A;Palombo M;Shemesh N;Zhang H;Alexander DC;Nilsson M;Froeling M;Biessels GJ;Zucchelli M;Frigo M;Albay E;Sedlar S;Alimi A;Deslauriers-Gauthier S;Deriche R;Fick R;Afzali M;Pieciak T;Bogusz F;Aja-Fernández S;Özarslan E;Jones DK;Chen H;Jin M;Zhang Z;Wang F;Nath V;Parvathaneni P;Morez J;Sijbers J;Jeurissen B;Fadnavis S;Endres S;Rokem A;Garyfallidis E;Sanchez I;Prchkovska V;Rodrigues P;Landman BA;Schilling KG
扩散MRI（dMRI）已成为评估脑组织微观结构组织的宝贵工具。根据特定的采集设置，dMRI信号对潜在扩散过程的特定属性进行编码。在过去的二十年里，已经提出了几种信号表示来拟合dMRI信号并解码这些属性。然而，大多数方法都是在有限的数据量上进行测试和开发的，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的dMRI信号表示的可推广性，以不同的扩散编码参数和脑组织类型。为此，我们组织了一个名为MEMENTO的社区挑战赛，提供相同的数据集，用于跨算法和技术的公平比较。我们考虑了两个最先进的扩散数据集，包括来自人脑的具有超过3820个独特扩散权重的单扩散编码（MDE）自旋回波数据（MASSIVE数据集），以及包括超过2520个独特数据点的小鼠大脑的双（振荡）扩散编码数据（DDE/DODE）。在5个不同体素中采样的数据的子集是公开分布的，并且要求挑战参与者预测数据的其余部分。一年后，八个参与团队提交了总共80个信号拟合。对于每个提交，我们评估了均方误差，预测误差的方差和贝叶斯信息标准。收到的提交预测多壳层的数据（37%）或DODE数据（22%），其次是carbohydrate数据（19%）和DDE（18%）。大多数提交的预测信号测量的干扰非常好，除了低和非常强的扩散权重。DDE和DODE数据的预测似乎更具挑战性，可能是因为没有提交明确解释扩散时间和频率。除了模型的选择，拟合过程和超参数的决定在预测性能中起着重要作用，突出了优化和报告这些选择的重要性。这项工作是一项社区努力，以突出该领域的优势和局限性，代表采用趋势编码方案获得的dMRI，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.
登录
查看更多内容
影响因子：
3.7
作者：
Blumenfeld-Katzir T;Pasternak O;Dagan M;Assaf Y
通讯作者：
Assaf Y
影响因子：
9.9
作者：
Baker, Laurie M.;Laidlaw, David H.;Paul, Robert H.
通讯作者：
Paul, Robert H.
影响因子：
5.7
作者：
Fick, Rutger H. J.;Wassermann, Demian;Deriche, Rachid
通讯作者：
Deriche, Rachid
影响因子：
3.5
作者：
Fick, Rutger H. J.;Wassermann, Demian;Deriche, Rachid
通讯作者：
Deriche, Rachid
影响因子：
3.3
作者：
Chang, LC;Jones, DK;Pierpaoli, C
通讯作者：
Pierpaoli, C