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Related Concept Videos

Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Jun 17, 2025

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function
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Arterial input function estimation compensating for inflow and partial voluming in dynamic contrast-enhanced MRI.

Chih-Hsien Tseng1,2,3, Martijn A Nagtegaal1,4, Matthias J P van Osch2,3,4

  • 1Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands.

NMR in Biomedicine
|August 6, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a new method to correct errors in arterial input function (AIF) measurements during dynamic contrast-enhanced MRI. The technique effectively compensates for inflow and partial volume effects, improving AIF accuracy for better pharmacokinetic analysis.

Keywords:
DCE MRIarterial input functioninflow effectpartial volume effect

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Pharmacokinetics

Background:

  • Arterial input function (AIF) measurement in dynamic contrast-enhanced (DCE) MRI is prone to errors from inflow and partial volume effects (PVE).
  • Inaccurate AIF can lead to significant errors in pharmacokinetic parameter estimation.
  • A previously introduced flow correction method estimates and compensates for spin inflow effects.

Purpose of the Study:

  • To investigate the efficiency of a flow correction method to simultaneously compensate for both inflow and PVE in AIF measurements.
  • To evaluate the method's performance using simulated and clinical DCE-MRI data.

Main Methods:

  • Simulated AIF data with varying levels of inflow and PVE contamination were generated.
  • Clinical AIFs were artificially contaminated with PVE by signal averaging.
  • The correction method was applied to simulated and clinical data, comparing corrected AIFs to true or original AIFs.
  • Peak, full width at half-maximum (FWHM), and area under curve (AUC) were compared.

Main Results:

  • Simulations showed minimal bias in peak, FWHM, and AUC of corrected AIFs.
  • Clinical data demonstrated highly correlated reconstructed curves across varying neighborhood sizes.
  • Internal carotid artery (ICA) and middle cerebral artery (MCA) AIFs showed similar characteristics, distinct from venous output function (VOF).

Conclusions:

  • The proposed method effectively compensates for both inflow and PVE simultaneously in AIF measurements.
  • Corrected AIFs offer a stable input for DCE-MRI analysis, potentially improving pharmacokinetic modeling accuracy.