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

Yi-Ru Lin1, Zheng-De Hong1, Shang-Yueh Tsai2,3

  • 1Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.

NMR in Biomedicine
|May 31, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for removing residual water signals in magnetic resonance spectroscopy (MRS) using Hankel Lanczos singular value decomposition (HLSVD) and a novel variance ratio. This approach optimizes component selection for accurate metabolite quantification in large-scale studies.

Keywords:
HLSVDMRSresidual waterwater suppression

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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.