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

TY - JOUR

T1 - Robust estimation of hemo-dynamic parameters in traditional DCE-MRI models

AU - Hansen, Mikkel Bo

AU - Tietze, Anna

AU - Haack, Søren

AU - Kallehauge, Jesper

AU - Mikkelsen, Irene Klærke

AU - Østergaard, Leif

AU - Mouridsen, Kim

PY - 2019/1/3

Y1 - 2019/1/3

N2 - Purpose 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. Methods 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 (v e ) from 19 glioma patients, we analyze differences in the number of un-physiological high-intensity v e values for both ETM and 2CXM, using a one-tailed paired t-test assuming un-equal variance. Results 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 v e values for the ETM (p<0.0001) and the 2CXM (p<0.0001). Conclusion 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.

AB - Purpose 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. Methods 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 (v e ) from 19 glioma patients, we analyze differences in the number of un-physiological high-intensity v e values for both ETM and 2CXM, using a one-tailed paired t-test assuming un-equal variance. Results 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 v e values for the ETM (p<0.0001) and the 2CXM (p<0.0001). Conclusion 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.

U2 - 10.1371/journal.pone.0209891

DO - 10.1371/journal.pone.0209891

M3 - Journal article

C2 - 30605459

SN - 1932-6203

VL - 14

SP - 1

EP - 17

JO - PLOS ONE

JF - PLOS ONE

IS - 1

M1 - e0209891

ER -