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Abstract #2176

Learned Proximal Convolutional Neural Network for Susceptibility Tensor Imaging

Kuo-Wei Lai1,2, Jeremias Sulam1, Manisha Aggarwal3, Peter van Zijl2,3, and Xu Li2,3
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, 2F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 3Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States

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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