等变球面卷积神经网络用于减少采集时间的新生儿扩散MRI中精确的纤维取向分布估计
Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time
AI总结：
本研究提出旋转等变球面卷积神经网络（sCNN），利用30%梯度方向的新生儿dMRI数据预测纤维取向分布，较MLP基线显著降低MSE并提高ACC，改善纤维追踪质量，实现高效准确的临床分析。
摘要：
利用扩散磁共振成像（dMRI）对新生儿大脑微结构进行早期准确评估，对于识别神经发育障碍至关重要，但由于信噪比（SNR）低、运动伪影以及持续的髓鞘化过程，这一任务仍具挑战性。在本研究中，我们提出了一种专为新生儿dMRI设计的旋转等变球面卷积神经网络（sCNN）框架。我们利用从完整协议中减少的梯度方向集（占完整协议的30%）采集的多壳层dMRI信号来预测纤维取向分布（FOD），从而实现更快、更具成本效益的采集。我们使用发展人类连接组计划（dHCP）提供的43个新生儿dMRI数据集的真实数据来训练和评估我们的sCNN性能。结果表明，与多层感知器（MLP）基线相比，sCNN实现了显著更低的均方误差（MSE）和更高的角相关系数（ACC），表明其在FOD估计中的准确性有所提高。此外，基于sCNN预测的FOD的纤维追踪结果显示，与基于MLP的结果相比，其解剖学合理性、覆盖范围和连贯性均有所改善。这些发现强调，sCNN凭借其固有的旋转等变性，为准确且临床高效的dMRI分析提供了一种有前景的方法，为改善早期大脑发育的诊断能力和表征铺平了道路。
英文摘要：
Early and accurate assessment of brain microstructure using diffusion Magnetic Resonance Imaging (dMRI) is crucial for identifying neurodevelopmental disorders in neonates, but remains challenging due to low signal-to-noise ratio (SNR), motion artifacts, and ongoing myelination. In this study, we propose a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework tailored for neonatal dMRI. We predict the Fiber Orientation Distribution (FOD) from multi-shell dMRI signals acquired with a reduced set of gradient directions (30% of the full protocol), enabling faster and more cost-effective acquisitions. We train and evaluate the performance of our sCNN using real data from 43 neonatal dMRI datasets provided by the Developing Human Connectome Project (dHCP). Our results demonstrate that the sCNN achieves significantly lower mean squared error (MSE) and higher angular correlation coefficient (ACC) compared to a Multi-Layer Perceptron (MLP) baseline, indicating improved accuracy in FOD estimation. Furthermore, tractography results based on the sCNN-predicted FODs show improved anatomical plausibility, coverage, and coherence compared to those from the MLP. These findings highlight that sCNNs, with their inherent rotational equivariance, offer a promising approach for accurate and clinically efficient dMRI analysis, paving the way for improved diagnostic capabilities and characterization of early brain development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
