This work demonstrates an unsupervised deep-learning approach to MRS(I) data denoising, incorporating a non-linear model without relying on MR-theoretical physical prior knowledge. The implemented autoencoder learns the underlying low-dimensional manifold in high-dimensional data vectors representing MRS(I) voxels. The method, developed for denoising water-fat spectroscopic images, was tested against both numerical and acquired phantoms containing milk cream. The proposed method shows results comparable with other techniques in boosting SNR and has been found more robust in low concentration component denoising. Deep learning data denoising for MRSI might result in faster acquisition, vital for MRSI clinical application.
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