  Generating high-quality synthetic data is crucial for addressing challenges
in medical imaging, such as domain adaptation, data scarcity, and privacy
concerns. Existing image quality metrics often rely on reference images, are
tailored for group comparisons, or are intended for 2D natural images, limiting
their efficacy in complex domains like medical imaging. This study introduces a
novel deep learning-based non-reference approach to assess brain MRI quality by
training a 3D ResNet. The network is designed to estimate quality across six
distinct artifacts commonly encountered in MRI scans. Additionally, a diffusion
model is trained on diverse datasets to generate synthetic 3D images of high
fidelity. The approach leverages several datasets for training and
comprehensive quality assessment, benchmarking against state-of-the-art metrics
for real and synthetic images. Results demonstrate superior performance in
accurately estimating distortions and reflecting image quality from multiple
perspectives. Notably, the method operates without reference images, indicating
its applicability for evaluating deep generative models. Besides, the quality
scores in the [0, 1] range provide an intuitive assessment of image quality
across heterogeneous datasets. Evaluation of generated images offers detailed
insights into specific artifacts, guiding strategies for improving generative
models to produce high-quality synthetic images. This study presents the first
comprehensive method for assessing the quality of real and synthetic 3D medical
images in MRI contexts without reliance on reference images.
