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Magnetic Resonance Imaging

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A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

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Published on: September 16, 2017

The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data.

M Heisen¹, X Fan, J Buurman

¹Biomedical Image Analysis, Eindhoven University of Technology, Den Dolech 2, WH 3.2a, PO Box 513, 5600 MB Eindhoven, The Netherlands. M.Heisen@tue.nl

Physics in Medicine and Biology

|August 4, 2010

Summary

This summary is machine-generated.

Data-derived arterial input functions (AIFs) improve pharmacokinetic modeling in breast cancer diagnosis using dynamic contrast-enhanced MRI (DCE-MRI). This method allows accurate parameter estimation even with low temporal resolution imaging data.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Biomedical Imaging
Quantitative Medical Imaging
Radiology

Background:

Pharmacokinetic modeling is a valuable quantitative analysis technique for cancer diagnosis.
Dynamic contrast-enhanced MRI (DCE-MRI) of the breast often uses low temporal resolution, limiting its clinical utility.
Arterial input functions (AIFs) are crucial for pharmacokinetic parameter estimation in DCE-MRI.

Purpose of the Study:

To investigate the impact of data-derived versus standard AIFs on pharmacokinetic parameter estimation at various temporal resolutions.
To determine if data-derived AIFs permit the use of lower temporal resolution data compared to standard AIFs.
To assess the accuracy of pharmacokinetic parameter estimation using data-derived AIFs with reduced temporal resolution.

Main Methods:

Downsampling high-temporal-resolution rodent DCE-MRI data using a k-space-based strategy.
Fitting the Tofts pharmacokinetic model using either a data-derived AIF or a standard literature-derived AIF.
Comparing pharmacokinetic parameter estimates (K(trans), v(e)) derived from low-temporal-resolution data with those from original high-temporal-resolution data.

Main Results:

Deviations in K(trans) and v(e) were significantly smaller when using data-derived AIFs compared to standard AIFs at lower temporal resolutions.
Lowering temporal resolution from 5s to 60s resulted in only a 2% change in K(trans) with data-derived AIFs (non-significant).
In contrast, using a standard AIF at 60s temporal resolution led to an 18% significant change in K(trans).

Conclusions:

Extracting the AIF from reference tissue data enables accurate pharmacokinetic parameter estimation from low-temporal-resolution DCE-MRI.
Data-derived AIFs enhance the robustness of pharmacokinetic modeling, improving the clinical utility of low-temporal-resolution breast DCE-MRI for cancer diagnosis.