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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords