Artificial Intelligence-Driven Image Quality Assessment for Intracranial Vessel Wall Magnetic Resonance Imaging

Image quality control is a prerequisite for quantitative image analysis. We develop a convolutional neural network-based model for assessing the image quality of intracranial vessel wall MRI. Experimental results show that the model prediction is in good agreement with a senior radiologist, with a Cohen’s Kappa of 0.689. The model demonstrates real-time evaluation speed which is 500 times faster than the radiologist. It has the potential to be used in performing quality control on historical data for research purposes, and also can be used to examine the image quality immediately after the clinical MRI scan.