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Uncertainty estimation in dynamic contrast-enhanced MRI
Anders Garpebring1, Patrik Brynolfsson, Jun Yu
1Division of Radiation Physics, Department of Radiation Sciences, Umeå University, Umeå, Sweden. anders.garpebring@radfys.umu.se
Abstract:
Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for K(trans) , ve , and vp , respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.
Insights
This study introduces a new method for calculating uncertainty maps for pharmacokinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI). The method accurately estimates uncertainties from common data errors, improving the reliability of DCE-MRI analysis in tumors.
Area of Science:
- Medical Imaging
- Biophysics
- Radiology
Background:
- Dynamic contrast-enhanced MRI (DCE-MRI) is used to estimate pharmacokinetic (PK) parameters reflecting physiological properties, particularly in tumors.
- Errors in DCE-MRI data propagate non-trivially to PK parameters, complicating interpretation.
- Quantifying these uncertainties is crucial for accurate clinical assessment.
Purpose of the Study:
- To develop and evaluate a method for calculating uncertainty maps of PK parameters derived from DCE-MRI.
- To assess the impact of various error sources on PK parameter uncertainty.
- To validate the proposed method using simulations and in vivo data.
Main Methods:
- A multivariate linear error propagation method was developed to calculate PK parameter uncertainty maps.
- The modified Kety model was used to investigate PK parameter uncertainties.
- Monte Carlo simulations and in vivo brain tumor data were employed for evaluation.
Main Results:
- The proposed method accurately estimated PK parameter uncertainties arising from noise in dynamic data.
- Uncertainties due to up to 15% signal/T1 map noise and arterial input function amplitude errors were well-estimated.
- The method showed less accuracy for errors in bolus arrival time, with significant disagreements for K(trans), ve, and vp.
Conclusions:
- The developed method offers an efficient way to calculate uncertainty maps for PK parameters in DCE-MRI.
- The method is applicable to various sources of uncertainty, enhancing the robustness of DCE-MRI analysis.
- Accurate uncertainty quantification is vital for reliable interpretation of DCE-MRI derived parameters in clinical settings.
