Keywords: Stroke, Stroke, vessel wall imagingImage 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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords