Dynamic deuterium MR spectroscopic imaging (DMRSI) is a powerful metabolic imaging method, with great potential for tumor imaging. However, current DMRSI applications are limited to low spatiotemporal resolutions due to low sensitivity. This work overcomes this issue using a machine learning-based method. The proposed method integrates subspace modeling with deep learning to effectively use prior information for sensitivity enhancement and thus enables high-resolution dynamic DMRSI. Experimental results have been obtained from rats with and without brain tumor, which demonstrate that we can obtain dynamic metabolic changes with unprecedented spatiotemporal resolutions.
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