Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation...

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Spectroscopy

Background:

• Magnetic resonance spectroscopy (MRS) provides metabolite information for diagnosis.
• Short echo time (TE) proton MRS offers better metabolite distinguishability but faces quantification challenges due to background signals from macromolecules (MM) and lipids.
• Accurate quantification is crucial for clinical applications of MRS.

Purpose of the Study:

• To introduce and evaluate an improved quantification algorithm, QUEST-MM (QUantitation based on quantum ESTimation with MM prior knowledge), for short TE proton MRS.
• To enhance metabolite quantification, automatic background estimation, and MM component quantification from single MRS acquisitions.
• To improve the clinical applicability of MRS, especially in studies involving pathological MM in the brain.

Main Methods:

• Development and testing of the QUEST-MM algorithm, incorporating prior knowledge of MM signals.
• Comparison of QUEST-MM with three QUEST-based strategies using bias-variance trade-off and Cramér-Rao lower bound estimates.
• Extensive Monte Carlo simulations to evaluate estimator performance, including metabolite and MM amplitude distributions.
• Application of QUEST-MM to in vivo 1H MRS data from rat and human brains using various pulse sequences and field strengths.

Main Results:

• QUEST-MM demonstrated superior performance compared to the QUEST (Subtract approach), offering better metabolite quantification.
• The algorithm successfully enabled automatic estimation of background signals and quantification of MM components.
• QUEST-MM proved to be a viable alternative to standard QUEST when measured MM signals are unavailable or unsuitable.
• Validation of QUEST-MM on diverse in vivo MRS datasets, including rat and human brain signals.

Conclusions:

• QUEST-MM significantly improves metabolite quantification in short TE proton MRS.
• The algorithm enhances the clinical utility of MRS by providing automatic background and MM quantification.
• QUEST-MM is a robust and valuable tool for MRS data analysis, particularly for studies involving brain pathologies affecting MM levels.
• The implementation of QUEST-MM in jMRUI will make it accessible for broader research and clinical use.