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Abstract #0243

MRSI spectral quantification using linear tangent space alignment (LTSA)-based manifold learning

Chao Ma1,2 and Georges El Fakhri1,2
1Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Radiology, Harvard Medical School, Boston, MA, United States


Spectral quantification is a critical step in quantitative MRS/MRSI. The recently proposed subspace-based spectral quantification method represents the spectral distribution of each model as a subspace model and enables effective use of spatiospectral priors to improve parametric estimation. However, modeling spectral distribution of each metabolite as a separate subspace leads to a large number of unknowns, which renders the resultant parametric estimation problem challenging when SNR is low. To address this issue, we propose a new linear tangent space alignment-based method for MRSI quantification, leveraging the intrinsic low-dimensional structure information of the underlying MRSI signals for improved parametric estimation.

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