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Image Quality Assessment (IQA) is of great value in the workflow of MagneticResonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especiallyrequired since high-quality reference MRI images are usually not available.Recently, many efforts have been devoted to developing deep learning-based BIQAapproaches. However, the performance of these methods is limited due to theutilization of simple content-non-adaptive network parameters and the waste ofthe important 3D spatial information of the medical images. To address theseissues, we design a 3D content-adaptive hyper-network for MRI BIQA. The overall3D configuration enables the exploration of comprehensive 3D spatialinformation from MRI images, while the developed content-adaptive hyper-networkcontributes to the self-adaptive capacity of network parameters and thus,facilitates better BIQA performance. The effectiveness of the proposed methodis extensively evaluated on the open dataset, MRIQC. Promising performance isachieved compared with the corresponding baseline and 4 state-of-the-art BIQAmethods. We make our code available at\url{https://git.openi.org.cn/SIAT_Wangshanshan/HyS-Net}.

Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network