Pharmacokinetic modeling strategies for dynamic hyperpolarized urea imaging

Area of Science:

• Medical Imaging
• Biophysics
• Pharmacokinetics

Background:

• Hyperpolarized 13C urea (HP urea) enables dynamic imaging of tissue metabolism and function.
• Quantifying tissue perfusion and permeability is crucial for disease diagnosis and monitoring.
• Pharmacokinetic (PK) modeling is essential for extracting quantitative physiological parameters from dynamic imaging data.

Purpose of the Study:

• To evaluate and compare the performance of different pharmacokinetic modeling methods for quantifying tissue perfusion and permeability using hyperpolarized 13C urea.
• To assess the accuracy and reproducibility of these models under various imaging conditions and noise levels.

Main Methods:

• Three PK models were developed: a multicompartment model (Model I) and two simplified models (Model II and Model III) that omit specific volume parameters.
• Models were evaluated using numerical simulations and experimental data from a thyroid cancer mouse model.
• The impact of acquisition settings, model simplifications, and noise on parameter estimation was quantified.

Main Results:

• Model I demonstrated reproducible and accurate quantification of perfusion/permeability parameters across a range of excitation angles and repetition times.
• Simplified Models II and III introduced bias in the estimation of the trans-capillary transfer rate constant (k_ve), particularly when vascular input function (VIF) amplitude was jointly estimated.
• Despite bias in individual parameters, the ratios k_ve/v_e and k_ve/v_b remained accurate for Models I and III, indicating robustness.

Conclusions:

• The study confirms the feasibility of using PK models with HP urea for quantitative assessment of tissue perfusion and permeability.
• Model I offers a robust approach, while simplified models may require careful consideration of their inherent biases for accurate physiological parameter estimation.