Model-based blind estimation of kinetic parameters in Dynamic Contrast Enhanced (DCE)-MRI

Jacob U. Fluckiger, Matthias Schabel, Edward V R DiBella

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with "truth" obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The "true" Ktrans values in tumor regions were not significantly different than the estimated values, 0.99 ± 0.41 and 0.86 ± 0.40 min-1, respectively, P = 0.27. "True" kep values also matched closely, 0.70 ± 0.24 and 0.65 ± 0.25 min-1, P = 0.08. When only tissue curves free of significant vascular contribution are used (vp <0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.

Original languageEnglish (US)
Pages (from-to)1477-1486
Number of pages10
JournalMagnetic Resonance in Medicine
Volume62
Issue number6
DOIs
StatePublished - Dec 2009
Externally publishedYes

Fingerprint

Pharmacokinetics
Neuroimaging
Sarcoma
Computer Simulation
Blood Vessels
Cluster Analysis
Noise
Neoplasms

Keywords

  • Arterial input function
  • Blind estimation
  • DCE-MRI
  • Perfusion imaging
  • Pharmacokinetic modeling
  • Tofts-Kety model

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Model-based blind estimation of kinetic parameters in Dynamic Contrast Enhanced (DCE)-MRI. / Fluckiger, Jacob U.; Schabel, Matthias; DiBella, Edward V R.

In: Magnetic Resonance in Medicine, Vol. 62, No. 6, 12.2009, p. 1477-1486.

Research output: Contribution to journalArticle

@article{d401b71065ab47cb886d262424e45015,
title = "Model-based blind estimation of kinetic parameters in Dynamic Contrast Enhanced (DCE)-MRI",
abstract = "A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with {"}truth{"} obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The {"}true{"} Ktrans values in tumor regions were not significantly different than the estimated values, 0.99 ± 0.41 and 0.86 ± 0.40 min-1, respectively, P = 0.27. {"}True{"} kep values also matched closely, 0.70 ± 0.24 and 0.65 ± 0.25 min-1, P = 0.08. When only tissue curves free of significant vascular contribution are used (vp <0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.",
keywords = "Arterial input function, Blind estimation, DCE-MRI, Perfusion imaging, Pharmacokinetic modeling, Tofts-Kety model",
author = "Fluckiger, {Jacob U.} and Matthias Schabel and DiBella, {Edward V R}",
year = "2009",
month = "12",
doi = "10.1002/mrm.22101",
language = "English (US)",
volume = "62",
pages = "1477--1486",
journal = "Magnetic Resonance in Medicine",
issn = "0740-3194",
publisher = "John Wiley and Sons Inc.",
number = "6",

}

TY - JOUR

T1 - Model-based blind estimation of kinetic parameters in Dynamic Contrast Enhanced (DCE)-MRI

AU - Fluckiger, Jacob U.

AU - Schabel, Matthias

AU - DiBella, Edward V R

PY - 2009/12

Y1 - 2009/12

N2 - A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with "truth" obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The "true" Ktrans values in tumor regions were not significantly different than the estimated values, 0.99 ± 0.41 and 0.86 ± 0.40 min-1, respectively, P = 0.27. "True" kep values also matched closely, 0.70 ± 0.24 and 0.65 ± 0.25 min-1, P = 0.08. When only tissue curves free of significant vascular contribution are used (vp <0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.

AB - A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with "truth" obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The "true" Ktrans values in tumor regions were not significantly different than the estimated values, 0.99 ± 0.41 and 0.86 ± 0.40 min-1, respectively, P = 0.27. "True" kep values also matched closely, 0.70 ± 0.24 and 0.65 ± 0.25 min-1, P = 0.08. When only tissue curves free of significant vascular contribution are used (vp <0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.

KW - Arterial input function

KW - Blind estimation

KW - DCE-MRI

KW - Perfusion imaging

KW - Pharmacokinetic modeling

KW - Tofts-Kety model

UR - http://www.scopus.com/inward/record.url?scp=73149107765&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=73149107765&partnerID=8YFLogxK

U2 - 10.1002/mrm.22101

DO - 10.1002/mrm.22101

M3 - Article

VL - 62

SP - 1477

EP - 1486

JO - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

SN - 0740-3194

IS - 6

ER -