PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
This addresses the challenge of accurate EP tomography in medical imaging, offering a novel approach for researchers in the field, though it appears incremental as it builds on physics-informed networks with specific adaptations.
The paper tackled the problem of reconstructing electrical properties (EP) and transmit fields from noisy and incomplete magnetic resonance measurements by proposing PIFON-EPT, a deep learning method that achieved ≤5% error in EP reconstruction and ≤1% error in denoising and completion using only 20% of noisy input data.
We propose Physics-Informed Fourier Networks for Electrical Properties (EP) Tomography (PIFON-EPT), a novel deep learning-based method for EP reconstruction using noisy and/or incomplete magnetic resonance (MR) measurements. Our approach leverages the Helmholtz equation to constrain two networks, responsible for the denoising and completion of the transmit fields, and the estimation of the object's EP, respectively. We embed a random Fourier features mapping into our networks to enable efficient learning of high-frequency details encoded in the transmit fields. We demonstrated the efficacy of PIFON-EPT through several simulated experiments at 3 and 7 tesla (T) MR imaging, and showed that our method can reconstruct physically consistent EP and transmit fields. Specifically, when only $20\%$ of the noisy measured fields were used as inputs, PIFON-EPT reconstructed the EP of a phantom with $\leq 5\%$ error, and denoised and completed the measurements with $\leq 1\%$ error. Additionally, we adapted PIFON-EPT to solve the generalized Helmholtz equation that accounts for gradients of EP between inhomogeneities. This yielded improved results at interfaces between different materials without explicit knowledge of boundary conditions. PIFON-EPT is the first method that can simultaneously reconstruct EP and transmit fields from incomplete noisy MR measurements, providing new opportunities for EPT research.