Keywords: Analysis/Processing, Spectroscopy, Brain, Deep learning, Quantification, RNN
Motivation:
Incomplete FIDs can be obtained due to the limited sampling windows as in spectroscopic MRF and SSFP-MRSI, or due to FID truncation for removing spectral artifact.
Goal(s): Developing a means of quantifying metabolites from incomplete FIDs will allow more efficient sequence design and better experimental outcome.
Approach: We developed a recurrent-neural-network (RNN) for metabolite quantification from incomplete FIDs at 3.0T. The RNN was trained on simulated data and tested on in vivo data.
Results: Although the performance of the RNN requires further improvement for low concentration metabolites (e.g., GABA), it may allow quantification of the major metabolites under highly limited sampling windows.
Impact: Incomplete FIDs can be obtained due to the limited sampling windows as in spectroscopic MRF and SSFP-MRSI. We developed a recurrent-neural-network, which can quantify the major metabolites from the initial 64 FID data points, thereby allowing more efficient sequence design.
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