  Magnetic resonance imaging (MRI) is an important non-invasive imaging method
in clinical diagnosis. Beyond the common image structures, parametric imaging
can provide the intrinsic tissue property thus could be used in quantitative
evaluation. The emerging deep learning approach provides fast and accurate
parameter estimation but still encounters the lack of network interpretation
and enough training data. Even with a large amount of training data, the
mismatch between the training and target data may introduce errors. Here, we
propose one way that solely relies on the target scanned data and does not need
a pre-defined training database. We provide a proof-of-concept that embeds the
physical rule of MRI, the Bloch equation, into the loss of physics-informed
neural network (PINN). PINN enables learning the Bloch equation, estimating the
T2 parameter, and generating a series of physically synthetic data.
Experimental results are conducted on phantom and cardiac imaging to
demonstrate its potential in quantitative MRI.
