Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?
Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?
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DOI：
10.1016/j.media.2015.10.012
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发表时间：
2015-12
影响因子：
10.9
通讯作者：
Rathi Y
中科院分区：
文献类型：
--
作者：
Ning L;Laun F;Gur Y;DiBella EV;Deslauriers-Gauthier S;Megherbi T;Ghosh A;Zucchelli M;Menegaz G;Fick R;St-Jean S;Paquette M;Aranda R;Descoteaux M;Deriche R;O'Donnell L;Rathi Y
扩散磁共振成像（dMRI）是研究体内脑白色物质连接和神经组织结构的首选模式。dMRI中的扩散加权信号反映了脑组织中水分子的扩散率，可用于产生用于临床研究的基于图像的生物标志物。由于扫描时间的限制，在临床上可行的扫描时间内可以采集有限数量的测量值。为了从离散的测量值集合重建dMRI信号，近年来已经结合变化的采样方案提出了大量算法，即，具有变化的b值和梯度方向。因此，必须在单个数据集上比较这些重建方法的性能，以便为神经科学家在设计其采集协议时做出明智的决定提供适当的指导。为此，SParse重建挑战赛（SParse Reconstruction Challenge）与计算扩散MRI研讨会（MICCAI 2014）一起沿着举行，以使用从物理体模采集的数据验证多种重建方法的性能。共有16个重建算法（9个团队）参加了本次社区挑战赛。目标是从稀疏的测量值集重建单个b值和/或多个b值数据。特别是，其目的是确定适当的采集协议（在测量次数，b值方面）和用于神经成像研究的分析方法。挑战并没有深入研究这些方法在估计模型特定指标（如各向异性分数（FA）或平均扩散率）时的准确性，而是研究这些方法拟合数据的准确性。本文提出了几个定量的结果有关的每一种重建算法。本文的结论为临床神经科学应用中选择合适的算法和相应的数据采样方案提供了有价值的指导。
Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SParse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.