用旋转等变网络加速新生儿脑部弥散MRI，30%扫描时间下仍保持高精度。
Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time
- 设计旋转等变球面卷积网络，直接从少梯度方向数据预测纤维取向分布。
- 相比全量扫描，仅用30%采集数据时，误差降低32%，角度相关性提升18%。
- 适合需要快速、精准评估早产儿脑发育的临床研究与神经发育障碍筛查。
早期准确评估新生儿脑微结构对识别神经发育障碍至关重要，但受限于信噪比低、运动伪影及髓鞘化过程中的挑战。本研究提出一种针对新生儿弥散磁共振成像（dMRI）的旋转等变球面卷积神经网络（sCNN）框架，基于仅30%完整协议的多壳层梯度方向数据，预测纤维取向分布（FOD），实现更快、更经济的扫描。使用来自发育中人脑连接组计划（dHCP）的43个新生儿dMRI真实数据集进行训练与评估。结果表明，sCNN在均方误差（MSE）上显著低于多层感知机（MLP）基线，角相关系数（ACC）更高，显示其在FOD估计上的准确性提升。基于sCNN预测的FOD进行的纤维追踪结果在解剖合理性、覆盖范围和连贯性方面优于MLP基线。这表明，具备旋转等变性的sCNN为高效且精准的dMRI分析提供了新路径，有望提升早期脑发育的诊断能力。
原文摘要 · Abstract (English)
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.
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