Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: The motivation behind this study is to severely shorten scan times for children undergoing cardiac examination.
Goal(s): The goal is to be able to create a 4D dataset from a short, free breathing real-time 2D stack of images.
Approach: We apply three machine learning models to the 2D stack. The first reconstructs the undersampled image data, the second corrects respiratory artefacts caused by free-breathing and the third model super resolves the images.
Results: The image quality is vastly improved after applying the machine learning models. The ventricular volumes are also in good agreement with the reference volumes.
Impact: Severely reduced scan times for comprehensive cardiac examination in CHD without the use of breath-holds. Machine Learning methods may be able to also be used for other imaging sequences, also resulting in faster image acquisition.
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