MRI has a broad range of applications due to its flexible acquisition capabilities. This demands profound knowledge and careful parameter adjustment to identify stable sets which guarantee a high image quality for various and, especially in examinations of patients, unpredictable conditions. This complex nature and long examination times make it susceptible to artifacts which can markedly reduce the diagnostic image quality. An early detection and possible correction of these artifacts is desired. In this work we propose a convolutional neural network to automatically detect, localize and quantify motion artifacts. Initial results in the head and abdomen demonstrate the method’s potential.
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