A Joint 2.5D Physics-coupled Deep learning based Polynomial Fitting Approach for MR Electrical Properties Tomography

Motivation: Conductivity reconstructions based on polynomial fitting methods are mostly 2D leading to inaccurate reconstructions as information arising from the through-plane dimension is missing. Goal(s): To include conductivity contributions from three-dimensions for deep-learning patch-based polynomial fitting reconstructions. Approach: A DL-informed polynomial fitting reconstruction method including $$$B_{1}^{+}$$$ magnitude information is presented. This method leverages neural networks to jointly predict optimal fitting coefficients enabling joint 2D-polynomial-fitting in three-orthogonal-planes, hence we call it 2.5D. Results: The proposed method demonstrates superior-performance compared to fitting-based 2D/3D fitting approaches and is computationally efficient for 3D-reconstructions. Impact: A 2.5-dimensional neural network informed fitting approach is used for MR-based conductivity reconstructions. Conductivity reconstruction accuracy as well as structural information are improved compared to physics-based and deep learning-based fitting methods.