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Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function
Published on: August 8, 2019
Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for
Geoff J M Parker1, Caleb Roberts, Andrew Macdonald
1Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK. geoff.parker@manchester.ac.uk
Magnetic Resonance in Medicine
|October 13, 2006
Summary
A new method for analyzing dynamic contrast-enhanced MRI (DCE-MRI) uses a general arterial input function (AIF) derived from multiple cancer patients. This approach improves the reliability of tumor microvasculature measurements, potentially enhancing cancer therapy monitoring.
Area of Science:
- Radiology
- Medical Imaging
- Oncology
Background:
- Dynamic contrast-enhanced MRI (DCE-MRI) is crucial for analyzing tumor microvasculature.
- Accurate measurement of the arterial input function (AIF) is essential for reliable DCE-MRI parameter quantification.
- Current AIF measurement methods can be challenging and time-consuming.
Purpose of the Study:
- To develop and validate a high-temporal-resolution population-based arterial input function (AIF) for DCE-MRI in cancer patients.
- To assess the impact of this generalized AIF on the reproducibility of DCE-MRI model parameters.
- To explore the potential of this method for improved sensitivity to therapy-induced changes in tumor microvasculature.
Main Methods:
- Acquisition of rapid T(1)-weighted 3D spoiled gradient-echo (GRE) datasets in 23 cancer patients across 113 visits.
- Automated extraction of AIF from gadodiamide-enhanced DCE-MRI scans.
- Generation of a representative mean AIF by combining individual patient AIFs.
- Analysis of DCE-MRI model parameters (K(trans), v(e), v(p)) using both individual and population AIFs.
Main Results:
- A generalized, high-temporal-resolution population AIF was successfully derived.
- Using the population AIF significantly improved the reproducibility of DCE-MRI model parameters (K(trans), v(e), v(p)).
- This enhanced reproducibility suggests increased sensitivity for detecting therapy-induced changes.
Conclusions:
- A novel, population-based AIF method enhances DCE-MRI analysis in cancer patients.
- This approach offers a robust alternative for AIF estimation, even when individual measurements are not feasible.
- The improved reproducibility holds promise for more sensitive monitoring of cancer treatment efficacy.
