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Magnetic resonance imaging (MRI) is an important non-invasive imaging methodin clinical diagnosis. Beyond the common image structures, parametric imagingcan provide the intrinsic tissue property thus could be used in quantitativeevaluation. The emerging deep learning approach provides fast and accurateparameter estimation but still encounters the lack of network interpretationand enough training data. Even with a large amount of training data, themismatch between the training and target data may introduce errors. Here, wepropose one way that solely relies on the target scanned data and does not needa pre-defined training database. We provide a proof-of-concept that embeds thephysical rule of MRI, the Bloch equation, into the loss of physics-informedneural network (PINN). PINN enables learning the Bloch equation, estimating theT2 parameter, and generating a series of physically synthetic data.Experimental results are conducted on phantom and cardiac imaging todemonstrate its potential in quantitative MRI.

Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging