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Abstract #0496

Direct estimation of metabolite maps from undersampled k-space data using linear tangent space alignment

Chao Ma1,2, Thibault Marin1,2, Paul K. Han1,2, and Georges El Fakhri1,2
1Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Radiology, Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Spectroscopy, Image Reconstruction, MRSI quantificationConventional MRSI methods perform MRSI image reconstruction and spectral quantification in two separate steps. This work presents a novel direct estimation method for MRSI that reconstructs high-resolution metabolite concentration maps from undersampled k-space data, leveraging a linear tangent space alignment (LTSA) model for spectral quantification and a low-rank (LR) model for denoising. Furthermore, the proposed framework allows estimating the temporal basis functions of the LR model from the undersampled, noise-corrupted k-space data, thus eliminating the need for experiment-dependent or pre-acquired spectral training data. The performance of the proposed method was validated using numerical simulation phantom and in vivo MRSI data.

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Keywords