We extended a Learned Proximal Convolutional-Neural-Network (LP-CNN) model used in scalar-based Quantitative Susceptibility Mapping (QSM) to tensor-based Susceptibility Tensor Imaging (STI). To improve the accuracy in reconstructed susceptibility anisotropy and tensor eigenvectors, we devised a decomposition loss function to balance training errors in isotropic and anisotropic components. Results using a synthetic dataset demonstrated that, compared to the conventional iterative approach, LP-CNN-STI provides better estimates of susceptibility tensor and smaller errors in anisotropy and eigenvectors. This deep learning-based STI method naturally incorporates the STI physical model, and is a first step toward development of learning-based STI potentially with less acquisition orientations.
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