  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 .
