Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-¹³C]Pyruvate

Area of Science:

• Medical Imaging
• Metabolic Imaging
• Biophysics

Background:

• Hyperpolarized 13C MRI enables real-time in vivo/in vitro study of glycolytic flow.
• Metabolite signal inconsistencies due to low signal-to-noise ratio (SNR) affect kinetic modeling.
• Accurate metabolite signal estimation is crucial for understanding disease-related metabolic changes.

Purpose of the Study:

• To develop and validate a novel method for accurate metabolite signal estimation in hyperpolarized 13C MRI.
• To improve the reliability of apparent exchange rate constants for dynamic metabolic studies.
• To enhance metabolic phenotyping of tumor cells for potential clinical applications.

Main Methods:

• Developed a method using a kinetic model and background noise estimation to refine metabolite signals.
• Evaluated the method through simulations and in vitro studies with photon-irradiated and control groups.
• Compared estimated exchange rate constants with raw signal values and 13C NMR Area-Under-Curve (AUC).

Main Results:

• The method significantly minimized metabolite signal fluctuations in simulations and in vitro experiments.
• Reliable apparent exchange rate constants were maintained, showing differences between irradiation and control groups.
• In vitro results using the new method showed consistency with 13C NMR measurements.

Conclusions:

• The developed method accurately measures metabolite signals, improving glycolytic flow analysis.
• Enhanced metabolite signals clarify metabolic phenotyping of tumor cells.
• This technique holds promise for personalized healthcare and patient stratification.