A Self-supervised Physics-informed Reconstruction Error Compensation Neural Network for Magnetic Resonance Electrical Property Tomography

Motivation: The recent physics-informed neural network (PINN) for Magnetic resonance electrical properties tomography (MREPT) still reply on ground truth as boundary conditions for back propagations. Goal(s): It is aimed to propose a PINN that uses only the residuals of an MREPT analytic model rather than ground truth data. Approach: A PINN framework which uses the aforementioned residuals to guide the network learning process of an neural network, enhancing the accuracy and reliability of the reconstruction, was proposed to compensate for the conductivity reconstruction errors of the Stabilized-EPT. Results: The results show increased accuracy of the reconstruction of conductivity for both normal and tumorous tissues. Impact: Feasibility of more accurate conductivity reconstruction without any ground truth information is demonstrated. This may lead to practical cancer detection.