用更真实的真值数据训练，可提升新生儿脑纤维方向估计精度。
Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains
- 用SS3T-CSD替代MSMT-CSD作为真值训练网络
- SS3T-CSD下单/多纤维体素比更符合新生儿脑实际
- 在不同年龄组间表现更稳定，适合早产儿研究
弥散磁共振成像（dMRI）是非侵入性描绘活体脑微观结构的方法。纤维方向分布（FODs）是广泛用于刻画白质纤维结构的数学表示。近年来，基于深度神经网络的FOD估计方法在新生儿中取得进展，尤其适用于少扩散测量的情况。这些方法通常以多壳多组织约束球形反卷积（MSMT-CSD）重构的FOD为目标真值，但可能不适用于发育中的大脑。本文通过在U-Net架构上分别使用MSMT-CSD和单壳三组织约束球形反卷积（SS3T-CSD）进行训练，验证了该假设。结果表明，与MSMT-CSD相比，SS3T-CSD下的单/多纤维体素比例更符合新生儿脑实际。此外，增加输入梯度方向数量能显著提升基于SS3T-CSD的性能。在年龄域偏移设置下，基于SS3T-CSD的模型在不同年龄组间保持鲁棒性，显示出其在新生儿脑成像中的潜力。
原文摘要 · Abstract (English)
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representations extensively used to map white matter fiber configurations. Recently, FOD estimation with deep neural networks has seen growing success, in particular, those of neonates estimated with fewer diffusion measurements. These methods are mostly trained on target FODs reconstructed with multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD), which might not be the ideal ground truth for developing brains. Here, we investigate this hypothesis by training a state-of-the-art model based on the U-Net architecture on both MSMT-CSD and single-shell three-tissue constrained spherical deconvolution (SS3T-CSD). Our results suggest that SS3T-CSD might be more suited for neonatal brains, given that the ratio between single and multiple fiber-estimated voxels with SS3T-CSD is more realistic compared to MSMT-CSD. Additionally, increasing the number of input gradient directions significantly improves performance with SS3T-CSD over MSMT-CSD. Finally, in an age domain-shift setting, SS3T-CSD maintains robust performance across age groups, indicating its potential for more accurate neonatal brain imaging.
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