Author:

Keywords:

BIOTENSORS - 339804;info:eu-repo/grantAgreement/EC/FP7/339804, Science & Technology, Technology, Engineering, Biomedical, Engineering, Canonical polyadic decomposition, magnetic resonance spectroscopic imaging, Lowner matrix, Hankel matrix, blind source separation, CANONICAL POLYADIC DECOMPOSITION, AUTOMATED QUANTITATION, MATRIX FACTORIZATION, PART II, UNIQUENESS, REMOVAL, SPECTRA, DIFFERENTIATION, Algorithms, Artifacts, Brain, Brain Neoplasms, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Signal Processing, Computer-Assisted, Water, STADIUS-17-73, 0801 Artificial Intelligence and Image Processing, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, Biomedical Engineering, 4003 Biomedical engineering, 4009 Electronics, sensors and digital hardware, 4603 Computer vision and multimedia computation

Abstract:

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.