Improving the arterial input function in dynamic contrast enhanced MRI by fitting the signal in the complex plane

Area of Science:

• Medical Imaging
• Oncologic Imaging
• Quantitative MRI

Background:

• Dynamic contrast-enhanced (DCE) imaging is crucial for oncologic imaging.
• Accurate arterial input function (AIF) estimation is essential for quantitative DCE-MRI.
• Current MR-based AIF estimation methods face significant challenges.

Purpose of the Study:

• To develop and validate a comprehensive model for improved AIF estimation in DCE-MRI.
• To utilize complex data, rather than just magnitude or phase, for enhanced AIF accuracy.
• To overcome limitations in current AIF estimation techniques.

Main Methods:

• A novel model using complex MRI data was developed for AIF estimation.
• The model was initially tested on simulated data.
• Validation was performed using phantom studies and DCE scans from 13 prostate cancer patients.

Main Results:

• The complex signal method improved the precision and accuracy of contrast agent concentration estimation.
• In vivo AIFs demonstrated good agreement between arterial voxels.
• The dynamic behavior of the estimated AIF closely correlated with DCE-CT results (mean CC: 0.92).

Conclusions:

• Using complex MRI signals enhances the accuracy and precision of AIF estimation.
• This advancement may facilitate patient-specific AIFs for improved quantitative DCE-MRI values.
• The findings suggest a potential for more reliable quantitative assessments in oncologic imaging.