Keywords: Quantitative Imaging, Quantitative Imaging
Motivation: Standard deep learning approaches provide fast and accurate parameter estimation in magnetic resonance imaging (MRI) but still suffer from lack of network interpretation and sufficient training data.
Goal(s): To propose one way that solely relies on the target scanned data and does not need a pre-defined training database with some Interpretability.
Approach: We provide a proof-of-concept that embeds Bloch equation of MRI into the loss of physics-informed neural network (PINN).
Results: PINN enables learning Bloch equation, estimating T2 parameter, and generating a series of physically synthetic data. T2 maps with phantom and realistic data obtained by PINN and least square are comparable.
Impact: The proposed method provides a new way to quantify tissue parameter, which does not require analytical formula of Bloch equation under specific sequences, and is expected to simplify the sequence design of quantitative magnetic resonance imaging.
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