Residual Water Suppression in MRS Using HLSVD With Automatic Component Selection Strategy

Area of Science:

• Magnetic Resonance Imaging
• Spectroscopy
• Biomedical Engineering

Background:

• Residual water signals in magnetic resonance spectroscopy (MRS) complicate metabolite quantification.
• Current methods like Hankel Lanczos singular value decomposition (HLSVD) require manual component selection, impacting reliability.
• Accurate metabolite analysis is crucial for neurological research and diagnostics.

Purpose of the Study:

• To develop an automated method for selecting optimal components in HLSVD for residual water removal in MRS.
• To improve the accuracy and consistency of metabolite quantification by minimizing baseline distortions.
• To validate the proposed method on both single voxel spectroscopy (SVS) and magnetic resonance spectroscopic imaging (MRSI) datasets.

Main Methods:

• An automated approach combining HLSVD with the residual water to metabolite variance ratio (RWVR) was developed.
• HLSVD was applied with component numbers ranging from 10 to 32; the minimum RWVR determined the optimal configuration.
• The Residual Water Index (variance ratio before and after removal) assessed suppression effectiveness on 3T SVS and MRSI data.

Main Results:

• The optimal component numbers were frequently identified within the 22-32 range, aligning with previous findings.
• The RWVR effectively indicated optimal component numbers, preventing unacceptable spectra with baseline distortions.
• Low Residual Water Index values confirmed effective residual water suppression across SVS and MRSI datasets.

Conclusions:

• The proposed automated method using RWVR for HLSVD component selection provides reliable and consistent residual water removal in MRS.
• This technique eliminates the need for manual tuning, making it suitable for large-scale studies.
• The approach enhances spectral quality and metabolite quantification accuracy in both SVS and MRSI.