A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging.
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging.
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DOI：
10.1016/j.media.2021.102129
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发表时间：
2021-08
影响因子：
10.9
通讯作者：
Gholipour A
中科院分区：
文献类型：
--
作者：
Karimi D;Vasung L;Jaimes C;Machado-Rivas F;Khan S;Warfield SK;Gholipour A
扩散加权磁共振成像测量的准确建模对于准确的大脑连接分析是必要的。现有的估计成像体素中束的数目和方向的方法要么依赖于对初始化和测量噪声敏感的非凸优化技术，要么容易预测虚假的束。在本文中，我们提出了一种基于机器学习的技术，可以准确地估计体素中束的数量和方向。我们的方法可以用模拟或真实的扩散加权成像数据来训练。我们的方法估计在单位球面上均匀分布的一组离散方向中每个方向到最近分束的角度。然后对该信息进行处理，以提取体素中束的数量和方向。在已知地面真实情况的真实模拟体模数据上，我们的方法比几种经典和机器学习方法更准确地预测交叉分束的数量和方向。它还导致了更准确的跟踪图像术。在实际数据上，我们的方法在对测量下采样的稳健性和对跟踪成像结果的专家质量评估方面优于或优于其他方法。
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.
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影响因子：
3.5
作者：
Garyfallidis E;Brett M;Amirbekian B;Rokem A;van der Walt S;Descoteaux M;Nimmo-Smith I;Dipy Contributors
通讯作者：
Dipy Contributors
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作者：
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影响因子：
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作者：
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通讯作者：
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