A knowledge-based approach to minimize baseline roll in chemical shift imaging

Area of Science:

• Magnetic Resonance Imaging
• Spectroscopy
• Biomedical Engineering

Background:

• Baseline roll is a common artifact in chemical shift imaging (CSI) data.
• This artifact arises from the summation of sinc functions and can distort spectral analysis.
• Existing methods may not fully address noise and phase errors associated with baseline roll.

Purpose of the Study:

• To develop and validate an automated method for minimizing baseline roll in CSI data.
• To improve the accuracy of spectral quantification by correcting for baseline artifacts.
• To introduce the BaseLine Artifact Suppression Technique (BLAST) routine.

Main Methods:

• A knowledge-based data processing approach in the frequency domain was employed.
• The algorithm computes "trough" and "ripple" components to model the artifact.
• A mirror component is created and added to the delayed spectrum, compensating for noise and zero-order phase errors.

Main Results:

• Simulated spectra showed peak heights and areas retained at 91-97% magnitude compared to nondelayed spectra.
• Clinical in vivo 31P MR spectra of the liver demonstrated corrected metabolite peaks at approximately 94% magnitude.
• The method produced good results on in vivo localized spectra with varying signal-to-noise ratios.

Conclusions:

• The BaseLine Artifact Suppression Technique (BLAST) routine effectively minimizes baseline roll in CSI.
• This automated method enhances spectral accuracy for both simulated and in vivo data.
• The BLAST routine offers a rapid (13s) and robust solution for spectral artifact correction.