Robust estimation of hemo-dynamic parameters in traditional DCE-MRI models

In dynamic contrast enhanced (DCE) MRI, separation of signal contributions from perfusion and leakage requires robust estimation of parameters in a pharmacokinetic model. We present and quantify the performance of a method to compute tissue hemodynamic parameters from DCE data using established phar...

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Vydáno v:PloS one Ročník 14; číslo 1; s. e0209891
Hlavní autoři: Hansen, Mikkel B., Tietze, Anna, Haack, Søren, Kallehauge, Jesper, Mikkelsen, Irene K., Østergaard, Leif, Mouridsen, Kim
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 03.01.2019
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Bayes Theorem
Bayesian analysis
Blood
Blood Volume
Brain cancer
Cancer therapies
Cervical cancer
Cervix
Clinical medicine
Computer simulation
Contrast Media - pharmacokinetics
Female
Glioma
Glioma - diagnostic imaging
Health physics
Hemodynamic monitoring
Hemodynamics
Hemodynamics - physiology
Hospitals
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - statistics & numerical data
Male
Mathematical models
Measurement techniques
Medical diagnosis
Medical imaging
Medicine
Medicine and Health Sciences
Neurosciences
NMR
Noise
Nuclear magnetic resonance
Parameter estimation
Parameter identification
Parameter robustness
Patients
Perfusion
Pharmacokinetics
Pharmacology
Physical Sciences
Physiology
Reproducibility of Results
Research and Analysis Methods
Uterine Cervical Neoplasms
Denmark
Aarhus Denmark
ISSN:1932-6203, 1932-6203
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Shrnutí:In dynamic contrast enhanced (DCE) MRI, separation of signal contributions from perfusion and leakage requires robust estimation of parameters in a pharmacokinetic model. We present and quantify the performance of a method to compute tissue hemodynamic parameters from DCE data using established pharmacokinetic models. We propose a Bayesian scheme to obtain perfusion metrics from DCE MRI data. Initial performance is assessed through digital phantoms of the extended Tofts model (ETM) and the two-compartment exchange model (2CXM), comparing the Bayesian scheme to the standard Levenberg-Marquardt (LM) algorithm. Digital phantoms are also invoked to identify limitations in the pharmacokinetic models related to measurement conditions. Using computed maps of the extra vascular volume (ve) from 19 glioma patients, we analyze differences in the number of un-physiological high-intensity ve values for both ETM and 2CXM, using a one-tailed paired t-test assuming un-equal variance. The Bayesian parameter estimation scheme demonstrated superior performance over the LM technique in the digital phantom simulations. In addition, we identified limitations in parameter reliability in relation to scan duration for the 2CXM. DCE data for glioma and cervical cancer patients was analyzed with both algorithms and demonstrated improvement in image readability for the Bayesian method. The Bayesian method demonstrated significantly fewer non-physiological high-intensity ve values for the ETM (p<0.0001) and the 2CXM (p<0.0001). We have demonstrated substantial improvement of the perceptive quality of pharmacokinetic parameters from advanced compartment models using the Bayesian parameter estimation scheme as compared to the LM technique.
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Competing Interests: KM and MBH are co-applicants on a patent application based on the presented technique (EP 13185195.8). MBH, LØ, and KM are shareholders in Cercare Medical ApS.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0209891