  Image Quality Assessment (IQA) is of great value in the workflow of Magnetic
Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially
required since high-quality reference MRI images are usually not available.
Recently, many efforts have been devoted to developing deep learning-based BIQA
approaches. However, the performance of these methods is limited due to the
utilization of simple content-non-adaptive network parameters and the waste of
the important 3D spatial information of the medical images. To address these
issues, we design a 3D content-adaptive hyper-network for MRI BIQA. The overall
3D configuration enables the exploration of comprehensive 3D spatial
information from MRI images, while the developed content-adaptive hyper-network
contributes to the self-adaptive capacity of network parameters and thus,
facilitates better BIQA performance. The effectiveness of the proposed method
is extensively evaluated on the open dataset, MRIQC. Promising performance is
achieved compared with the corresponding baseline and 4 state-of-the-art BIQA
methods. We make our code available at
\url{https://git.openi.org.cn/SIAT_Wangshanshan/HyS-Net}.
