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Blind estimation of the arterial input function in dynamic contrast-enhanced MRI using purity maximization.
Yu-Chun Lin1, Tsung-Han Chan, Chong-Yung Chi
1Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Taiwan.
Magnetic Resonance in Medicine
|March 3, 2012
Summary
A novel blind source separation algorithm accurately estimates the arterial input function (AIF) in dynamic contrast-enhanced MRI, minimizing errors from partial volume effects for reliable kinetic parameter quantification.
Area of Science:
- Medical Imaging
- Biophysics
- Pharmacokinetics
Background:
- Arterial input function (AIF) estimation is critical for accurate dynamic contrast-enhanced (DCE) MRI quantification.
- Partial volume effects and contamination introduce significant errors in AIF estimation.
- Existing methods often struggle with precise AIF determination, impacting downstream analyses.
Purpose of the Study:
- To develop and validate a blind source separation (BSS) algorithm for robust AIF estimation in DCE-MRI.
- To assess the impact of partial volume effects on AIF purity and kinetic parameter accuracy.
- To evaluate the reproducibility and feasibility of the BSS-AIF method in clinical and preclinical settings.
Main Methods:
- A BSS algorithm was employed to identify the voxel time course with maximal purity, representing minimal partial volume contamination.
- Simulations were conducted to evaluate AIF purity, estimation accuracy, and the influence of purity on kinetic parameters (K(trans), V(e), V(p)).
- In vivo DCE-MRI data from hypopharyngeal cancer patients and brain tumor-bearing rats were analyzed.
Main Results:
- Simulations demonstrated that the highest purity AIF closely approximated the true AIF.
- Manual AIF selection in patients resulted in reduced purity, leading to biased kinetic parameter estimates (underestimation of K(trans) and V(e), overestimation of V(p)).
- Tumor kinetic parameters were more sensitive to purity variations than muscle parameters; animal experiments confirmed BSS-AIF parameter reproducibility.
Conclusions:
- The BSS method is a feasible and reproducible approach for identifying the AIF voxel with the most accurate tracer concentration time course.
- This BSS-AIF method significantly improves the accuracy of DCE-MRI quantification by mitigating partial volume effects.
- The findings support the clinical utility of BSS for enhancing the reliability of DCE-MRI-derived biomarkers.
