Slice profile effects on quantitative analysis of hyperpolarized pyruvate

Area of Science:

• Biomedical Imaging
• Metabolic Imaging
• Quantitative MRI

Background:

• Hyperpolarized pyruvate magnetic resonance imaging (MRI) offers a novel biomarker for cancer metabolism.
• This technique relies on tracking the in vivo conversion of pyruvate to lactate.
• Robust quantification methods are crucial for reliable clinical application.

Purpose of the Study:

• To investigate potential biases in kinetic analysis of hyperpolarized pyruvate imaging due to assumed uniform slice excitation profiles.
• To develop and validate a method for correcting these slice profile-induced biases.

Main Methods:

• Utilized a Bloch-McConnell simulator to generate synthetic data with known pharmacokinetic parameters.
• Compared kinetic analysis assuming ideal slice profiles versus incorporating actual slice profile information.
• Developed a slice profile-correction algorithm by subdividing slices to account for variable excitation angles.

Main Results:

• Identified a mismatch between acquisition and analysis slice profiles as a source of quantification bias.
• Demonstrated that imperfect slice profiles lead to overestimation of the metabolic exchange rate.
• Validated the correction algorithm in simulations, phantom studies, and patient data (prostate cancer).

Conclusions:

• Slice profile biases significantly impact hyperpolarized pyruvate kinetic analysis.
• The developed correction algorithm effectively minimizes these biases, enhancing quantification accuracy.
• This method is applicable to both single and multislice acquisitions, improving the reliability of metabolic imaging in oncology.