Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using...

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Imaging Analysis
• Biomedical Engineering

Background:

• Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for assessing tissue perfusion and vascularity.
• Accurate quantification of contrast agent (CA) concentration is essential for reliable DCE-MRI analysis.
• Current methods for CA quantification can be affected by noise and bias, limiting diagnostic accuracy.

Purpose of the Study:

• To develop and evaluate a novel method for enhanced contrast agent (CA) quantification in DCE-MRI using simulations.
• To investigate the effectiveness of combining phase signal with magnitude data for improved CA estimation.
• To assess the impact of noise levels and magnetic field strength on the proposed quantification method.

Main Methods:

• A maximum likelihood estimator was developed, integrating DCE-MRI phase signal with an independent CA estimate (e.g., from magnitude data).
• Extensive simulations were conducted to evaluate the estimator's performance in reducing bias and noise in CA concentration estimates.
• Simulations covered a range of noise levels, from body coil to head coil sensitivity, at both 1.5T and 3T field strengths.

Main Results:

• The proposed method significantly reduced the root mean squared error (RMSE) in bolus peak CA concentration from 2.24 to 0.11 mM in vessels and 0.16 to 0.08 mM in the tumor rim at 3T with head coil noise levels.
• Substantial improvements in CA quantification accuracy were observed under high signal-to-noise conditions.
• No significant improvements were noted in tissues with low CA uptake, such as white matter.

Conclusions:

• Incorporating phase information into DCE-MRI analysis demonstrably reduces errors in estimated CA concentrations.
• Higher magnetic field strengths and greater CA concentrations enhance the phase response, leading to improved quantification accuracy.
• Further research is needed to address challenges like background phase drift for successful in vivo application of this method.