title: Comparison of analytical and numerical analysis of the reference region model for DCE-MRI.
abstract: This study compared three methods for analyzing DCE-MRI data with a reference region (RR) model: a linear least-square fitting with numerical analysis (LLSQ-N), a nonlinear least-square fitting with numerical analysis (NLSQ-N), and an analytical analysis (NLSQ-A). The accuracy and precision of estimating the pharmacokinetic parameter ratios KR and VR, where KR is defined as a ratio between the two volume transfer constants, K(trans,TOI) and K(trans,RR), and VR is the ratio between the two extracellular extravascular volumes, ve,TOI and ve,RR, were assessed using simulations under various signal-to-noise ratios (SNRs) and temporal resolutions (4, 6, 30, and 60s). When no noise was added, the simulations showed that the mean percent error (MPE) for the estimated KR and VR using the LLSQ-N and NLSQ-N methods ranged from 1.2% to 31.6% with various temporal resolutions while the NLSQ-A method maintained a very high accuracy (<1.0×10(-4) %) regardless of the temporal resolution. The simulation also indicated that the LLSQ-N and NLSQ-N methods appear to underestimate the parameter ratios more than the NLSQ-A method. In addition, seven in vivo DCE-MRI datasets from spontaneously occurring canine brain tumors were analyzed with each method. Results for the in vivo study showed that KR (ranging from 0.63 to 3.11) and VR (ranging from 2.82 to 19.16) for the NLSQ-A method were both higher than results for the other two methods (KR ranging from 0.01 to 1.29 and VR ranging from 1.48 to 19.59). A temporal downsampling experiment showed that the averaged percent error for the NLSQ-A method (8.45%) was lower than the other two methods (22.97% for LLSQ-N and 65.02% for NLSQ-N) for KR, and the averaged percent error for the NLSQ-A method (6.33%) was lower than the other two methods (6.57% for LLSQ-N and 13.66% for NLSQ-N) for VR. Using simulations, we showed that the NLSQ-A method can estimate the ratios of pharmacokinetic parameters more accurately and precisely than the NLSQ-N and LLSQ-N methods over various SNRs and temporal resolutions. All simulations were validated with in vivo DCE MRI data.
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
authors.forename: Joonsang
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA.
authors.forename: Julio
authors.initials: J
authors.lastname: Cárdenas-Rodríguez
authors.affiliation: Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA; Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ, USA.
authors.forename: Mark D
authors.initials: MD
authors.lastname: Pagel
authors.affiliation: College of Veterinary Medicine, The University of Georgia, Athens, GA, USA.
authors.forename: Simon
authors.initials: S
authors.lastname: Platt
authors.affiliation: College of Veterinary Medicine, The University of Georgia, Athens, GA, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Kent
authors.affiliation: Department of Physics and Astronomy, The University of Georgia, Athens, GA, USA; BioImaging Research Center (BIRC), The University of Georgia, Athens, GA, USA. Electronic address: qzhao@physast.uga.edu.
authors.forename: Qun
authors.initials: Q
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D001921:Brain / Q000378:metabolism / Q000473:pathology*; D001932:Brain Neoplasms / Q000378:metabolism / Q000473:pathology*; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D003627:Data Interpretation, Statistical; D005682:Gadolinium / Q000493:pharmacokinetics; D006801:Humans; D007089:Image Enhancement / Q000379:methods / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D008954:Models, Biological; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D005682:Gadolinium
keywords: Analytical analysis; DCE-MRI; Numerical analysis; Pharmacokinetic parameter ratio; Reference region model; Regression analysis