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Diffusion-weighted (DW) MRI measures the direction and scale of the localdiffusion process in every voxel through its spectrum in q-space, typicallyacquired in one or more shells. Recent developments in micro-structure imagingand multi-tissue decomposition have sparked renewed attention to the radialb-value dependence of the signal. Applications in tissue classification andmicro-architecture estimation, therefore, require a signal representation thatextends over the radial as well as angular domain. Multiple approaches havebeen proposed that can model the non-linear relationship between the DW-MRIsignal and biological microstructure. In the past few years, many deeplearning-based methods have been developed towards faster inference speed andhigher inter-scan consistency compared with traditional model-based methods(e.g., multi-shell multi-tissue constrained spherical deconvolution). However,a multi-stage learning strategy is typically required since the learningprocess relied on various middle representations, such as simple harmonicoscillator reconstruction (SHORE) representation. In this work, we present aunified dynamic network with a single-stage spherical convolutional neuralnetwork, which allows efficient fiber orientation distribution function (fODF)estimation through heterogeneous multi-shell diffusion MRI sequences. We studythe Human Connectome Project (HCP) young adults with test-retest scans. Fromthe experimental results, the proposed single-stage method outperforms priormulti-stage approaches in repeated fODF estimation with shell dropoff andsingle-shell DW-MRI sequences.

A Unified Single-stage Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-weighted MRI