Literature DB >> 17654583

On the identifiability of pharmacokinetic parameters in dynamic contrast-enhanced imaging.

Richard G P Lopata1, Walter H Backes, Paul P J van den Bosch, Natal A W van Riel.   

Abstract

The so-called "Kety model" is a two-compartment pharmacokinetic model describing tumor perfusion kinetics. Its parameters, the transendothelial transfer constant (K(trans)), extravascular extracellular volume fraction (upsilon(e)), and microvascular plasma volume fraction (upsilon(p)), can be estimated with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the results obtained by current methods show large variation in predictability and reliability. Here, the aim was to examine which experimental conditions have to be fulfilled to avoid large uncertainties and mutual dependencies of the parameters. Using frequency response analysis and simulation, the identifiability of the model was examined. The requirements and influence of contrast enhancement measurements, such as temporal resolution, signal to noise ratio, and contrast injection rate, on the accuracy of the parameters were analyzed. Tissue response characteristics revealed a low-frequency system with a cutoff frequency equal to K(trans)/upsilon(e), which confines the required temporal resolution. For malignant tissue with hyperpermeable vasculature (high K(trans)) a higher sampling frequency is required to accurately estimate K(trans) than for normal tissue. Too low sampling rates or too low injection rates resulted in inaccurate K(trans) values and hereby unreliable classification of malignant tissue.

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Year:  2007        PMID: 17654583     DOI: 10.1002/mrm.21336

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  13 in total

1.  Incorporating a vascular term into a reference region model for the analysis of DCE-MRI data: a simulation study.

Authors:  A Z Faranesh; T E Yankeelov
Journal:  Phys Med Biol       Date:  2008-04-25       Impact factor: 3.609

2.  Precision analysis of kinetic modelling estimates in dynamic contrast enhanced MRI.

Authors:  Dieter De Naeyer; Yves De Deene; Wim P Ceelen; Patrick Segers; Pascal Verdonck
Journal:  MAGMA       Date:  2011-01-08       Impact factor: 2.310

3.  A novel framework for evaluating the image accuracy of dynamic MRI and the application on accelerated breast DCE MRI.

Authors:  Yuan Le; Marcel Dominik Nickel; Stephan Kannengiesser; Berthold Kiefer; Bruce Spottiswoode; Brian Dale; Victor Soon; Chen Lin
Journal:  MAGMA       Date:  2017-09-11       Impact factor: 2.310

4.  Tumor metabolism and perfusion in head and neck squamous cell carcinoma: pretreatment multimodality imaging with 1H magnetic resonance spectroscopy, dynamic contrast-enhanced MRI, and [18F]FDG-PET.

Authors:  Jacobus F A Jansen; Heiko Schöder; Nancy Y Lee; Hilda E Stambuk; Ya Wang; Matthew G Fury; Snehal G Patel; David G Pfister; Jatin P Shah; Jason A Koutcher; Amita Shukla-Dave
Journal:  Int J Radiat Oncol Biol Phys       Date:  2011-01-13       Impact factor: 7.038

5.  Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation.

Authors:  Jiazheng Shi; Berkman Sahiner; Heang-Ping Chan; Chintana Paramagul; Lubomir M Hadjiiski; Mark Helvie; Thomas Chenevert
Journal:  Med Phys       Date:  2009-11       Impact factor: 4.071

6.  Sensitivity of quantitative metrics derived from DCE MRI and a pharmacokinetic model to image quality and acquisition parameters.

Authors:  Yue Cao; Diana Li; Zhou Shen; Daniel Normolle
Journal:  Acad Radiol       Date:  2010-04       Impact factor: 3.173

7.  Optimal acquisition and modeling parameters for accurate assessment of low Ktrans blood-brain barrier permeability using dynamic contrast-enhanced MRI.

Authors:  Samuel R Barnes; Thomas S C Ng; Axel Montagne; Meng Law; Berislav V Zlokovic; Russell E Jacobs
Journal:  Magn Reson Med       Date:  2015-06-16       Impact factor: 4.668

8.  Pharmacokinetic mapping for lesion classification in dynamic breast MRI.

Authors:  Matthias C Schabel; Glen R Morrell; Karen Y Oh; Cheryl A Walczak; R Brad Barlow; Leigh A Neumayer
Journal:  J Magn Reson Imaging       Date:  2010-06       Impact factor: 4.813

9.  Subchondral fluid dynamics in a model of osteoarthritis: use of dynamic contrast-enhanced magnetic resonance imaging.

Authors:  J H Lee; J P Dyke; D Ballon; D M Ciombor; M P Rosenwasser; R K Aaron
Journal:  Osteoarthritis Cartilage       Date:  2009-04-17       Impact factor: 6.576

10.  Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.

Authors:  Yannick Bliesener; Jay Acharya; Krishna S Nayak
Journal:  IEEE Trans Med Imaging       Date:  2019-11-26       Impact factor: 10.048

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