Under-sampling trajectory design for compressed sensing based DCE-MRI.

Area of Science:

• Medical Imaging
• Biophysics
• Radiology

Background:

• Dynamic contrast-enhanced MRI (DCE-MRI) requires high temporal and spatial resolution for accurate tumor vasculature characterization.
• Compressed Sensing (CS) offers potential for improved DCE-MRI but traditional variable density (VD) undersampling can introduce uncertainty in parameter estimation.
• Existing VD schemes often necessitate multiple parameter adjustments and reconstructions, limiting their applicability in DCE-MRI.

Purpose of the Study:

• To develop a robust CS undersampling trajectory design for DCE-MRI.
• To enhance the accuracy and reliability of kinetic parameter estimation.
• To overcome limitations associated with traditional VD schemes in DCE-MRI.

Main Methods:

• A novel undersampling trajectory design was developed by adaptively segmenting k-space into low- and high-frequency domains.
• The variable density (VD) scheme was applied exclusively to the high-frequency domain.
• The proposed method was evaluated for robustness against changes in Probability Density Function (PDF) parameters and inherent randomness.

Main Results:

• The novel undersampling trajectory demonstrated high accuracy in kinetic parameter estimation.
• The proposed method showed superior robustness compared to the traditional VD design, even with fixed PDF parameters.
• Simulation results confirmed the effectiveness of the adaptive k-space segmentation strategy.

Conclusions:

• The developed undersampling trajectory design offers a more accurate and robust approach for DCE-MRI.
• This method addresses the limitations of traditional VD schemes, making it suitable for demanding DCE-MRI applications.
• The findings suggest a significant advancement in quantitative imaging for tumor characterization.