Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Super-resolutionCEST is an important source of new contrast in MRI. However, it is time-consuming to obtain high-resolution CEST images. We proposed a deep learning based Two-Stage Super-Resolution (TSSR) model for CEST images. Compared with conventional SISR or MFSR models, TSSR model can effectively utilize the correlations among slices and those among saturation offsets for CEST images. We acquired brain CEST images on 14 volunteers using a 3T clinical MR scanner. Initial results suggested that the proposed TSSR model outperformed other methods for all the evaluation indicators. Our work showed the potential in reconstruction of high-resolution CEST images from low-resolution ones.
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