Fibre orientation distribution (FOD) reconstruction using deep learning has the potential to produce accurate FODs from a reduced number of diffusion-weighted images (DWIs), decreasing total imaging time. Diffusion acquisition invariant representations of the DWI signals are typically used as input to these methods to ensure that they can be applied flexibly to data with different b-vectors and b-values; however, this means the network cannot condition its output directly on the DWI signal. In this work, we propose a spherical deconvolution network, a model-driven deep learning FOD reconstruction architecture, that ensures intermediate and output FODs produced by the network are consistent with the input DWI signals. Furthermore, we implement a fixel classification penalty within our loss function, encouraging the network to produce FODs that can subsequently be segmented into the correct number of fixels and improve downstream fixel-based analysis. Our results show that the model-based deep learning architecture achieves competitive performance compared to a state-of-the-art FOD super-resolution network, FOD-Net. Moreover, we show that the fixel classification penalty can be tuned to offer improved performance with respect to metrics that rely on accurately segmented of FODs. Our code is publicly available at https://github.com/Jbartlett6/SDNet .

翻译：纤维取向分布（FOD）重建利用深度学习技术，有望从少量弥散加权图像（DWI）中生成精确的FOD，从而缩短总成像时间。此类方法通常采用弥散采集不变表示的DWI信号作为输入，以确保其能灵活应用于不同b-向量和b-值的数据；然而，这意味着网络无法直接基于DWI信号条件化其输出。本研究提出一种球面反卷积网络——一种模型驱动的深度学习FOD重建架构，该架构确保网络生成的中间和最终FOD与输入DWI信号保持一致。此外，我们在损失函数中引入纤维簇分类惩罚项，促使网络生成的FOD能够被后续正确分割为相应数量的纤维簇，从而提升下游基于纤维簇的分析性能。结果表明，与现有最先进的FOD超分辨率网络FOD-Net相比，基于模型的深度学习架构取得了具有竞争力的性能。同时，我们证明纤维簇分类惩罚项可通过调整优化依赖于FOD准确分割的性能指标。我们的代码已在 https://github.com/Jbartlett6/SDNet 公开。