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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
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Published on: April 18, 2015

Quantitative dynamic contrast-enhanced MRI for mouse models using automatic detection of the arterial input function.

Jae-Hun Kim1, Geun Ho Im, Jehoon Yang

  • 1Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.

NMR in Biomedicine
|September 29, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for detecting the arterial input function (AIF) in dynamic contrast-enhanced MRI (DCE-MRI) mouse cancer models. The new algorithm accurately quantifies pharmacokinetic parameters, aiding cancer research.

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Area of Science:

  • Biomedical Imaging
  • Preclinical Cancer Research
  • Pharmacokinetics

Background:

  • Dynamic contrast-enhanced MRI (DCE-MRI) is vital for cancer evaluation and preclinical research.
  • Accurate quantification of pharmacokinetic parameters in DCE-MRI requires determining the arterial input function (AIF).
  • Manual AIF determination in mouse models is challenging due to small dimensions and tumor proximity.

Purpose of the Study:

  • To develop and validate an automated algorithm for AIF detection in mouse DCE-MRI.
  • To assess the algorithm's accuracy in quantifying pharmacokinetic parameters compared to manual methods.
  • To improve the efficiency and reliability of DCE-MRI analysis in cancer research.

Main Methods:

  • An algorithm utilizing Kendall's coefficient of concordance for automatic AIF detection was proposed.
  • The method was validated using computer simulations and applied to DCE-MR images of tumor-bearing mice (n=8).
  • Pharmacokinetic parameters (Ktrans, ve, vp, kep) were computed and compared between automated and manual AIF determination.

Main Results:

  • Computer simulations demonstrated the algorithm's ability to categorize simulated AIF signals based on noise levels.
  • Pharmacokinetic parameters derived from the automated AIF were comparable to manual determination.
  • Acceptable differences were observed for Ktrans (5.14 ± 3.60%), ve (6.02 ± 3.22%), vp (5.10 ± 7.05%), and kep (5.38 ± 4.72%).

Conclusions:

  • The proposed automated AIF detection method using Kendall's coefficient of concordance is effective for quantitative DCE-MRI in mouse cancer models.
  • This automated approach offers a reliable alternative to manual AIF determination, enhancing preclinical cancer research.
  • The algorithm facilitates more efficient and accurate pharmacokinetic analysis, supporting therapeutic response monitoring.