Modification of population based arterial input function to incorporate individual variation

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Radiology

Background:

• Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is crucial for assessing tissue perfusion.
• Arterial Input Function (AIF) is essential for quantitative analysis in DCE-MRI but is susceptible to noise and pulsatility.
• Population-based AIF (pAIF) offers high signal-to-noise ratio (SNR) but lacks individual variability, limiting its clinical applicability.

Purpose of the Study:

• To develop and validate a method for modifying a population-based arterial input function (pAIF) to incorporate individual variations.
• To improve the accuracy of DCE-MRI analysis by creating a high-SNR AIF that reflects individual physiological differences.

Main Methods:

• A novel technique was developed to scale a pAIF in time and amplitude based on individual patient AIF characteristics.
• Individual AIF variations, linked to cardiac output and blood volume, were identified using full width at half maximum (FWHM) and amplitude.
• The modified pAIF was validated using DCE-MRI data from 18 prostate cancer patients.

Main Results:

• The initial root mean square error (RMSE) between the standard pAIF and individual AIFs was 0.88±0.48mM.
• The proposed modification significantly reduced the RMSE to 0.25±0.11mM (p<0.0001), indicating improved accuracy.
• The modified pAIF effectively incorporates individual physiological variations while maintaining high SNR.

Conclusions:

• The proposed method successfully modifies a pAIF to accurately reflect individual patient variations in DCE-MRI.
• This technique offers a robust approach to obtaining high-SNR, individualized AIFs for more precise quantitative analysis in DCE-MRI.
• The improved AIF accuracy has significant implications for cancer imaging and other applications relying on DCE-MRI.