Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction
Motivation: Cardiac motion can lead to long acquisition times and poor T1 mapping quality. Its complexity makes correction difficult. Although deep learning methods address this problem, they usually require training data, which is rarely available.
Goal(s): Efficient cardiac T1 mapping requiring only 2s acquisition-time per slice, utilizing deep learning for cardiac motion correction without the need of training data.
Approach: This method combines classical iterative reconstruction methods and Zero-Shot self-supervised CNNs for motion estimation and regularization. Being subject-specific, it does not require any training dataset.
Results: T1 maps from the proposed method show improved details in the structure of myocardium and papillary muscles.
Impact: The proposed method represents a valid alternative to supervised deep learning quantitative reconstruction methods, by employing the advantages of deep learning techniques without the need of target or training data, which is often unavailable.
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