Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceDiffusion tensor imaging (DTI) needs a large number of diffusion-weighted images (DWIs) to reliably reconstruct the diffusion measurements of the brain white matter, making the data acquisition time-consuming. Deep learning has emerged as a powerful technique to reduce the number of acquired DWIs. While most existing deep learning methods are supervised and need high-quality ground truth data as the training labels. Here, we proposed an unsupervised and subject-specific DTI reconstruction method called DTI-Net to significantly reduce the required number of DWIs, while also can simultaneously conduct the super-resolution reconstruction of the tensors.
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