Tensor Based Method for Residual Water Suppression in H Magnetic Resonance Spectroscopic Imaging.

OBJECTIVE: Magnetic resonance spectroscopic imaging (MRSI) signals are often corrupted by residual water and artifacts. Residual water suppression plays an important role in accurate and efficient quantification of metabolites from MRSI. A tensor-based method for suppressing residual water is proposed.

METHODS: A third-order tensor is constructed by stacking the Lowner matrices corresponding to each MRSI voxel spectrum along the third mode. A canonical polyadic decomposition (CPD) is applied on the tensor to extract the water component, and to subsequently remove it from the original MRSI signals.

RESULTS: The proposed method applied on both simulated and in-vivo MRSI signals showed good water suppression performance.

CONCLUSION: The tensor-based Lowner method has better performance in suppressing residual water in MRSI signals as compared to the widely-used subspace-based Hankel singular value decomposition (HSVD) method.

SIGNIFICANCE: A tensor method suppresses residual water simultaneously from all the voxels in the MRSI grid and helps in preventing the failure of the water suppression in single voxels.