Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI.
Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision.



The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast-enhanced magnetic resonance imaging.



Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast-to-noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95).



The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise. Magn Reson Med 77:2414-2423, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Institute of Biomedical Engineering, Department of Engineering Science University of Oxford, Oxford, United Kingdom.
Jesper F
JF
Kallehauge
Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.
Steven
S
Sourbron
Institute of Biomedical Engineering, Department of Engineering Science University of Oxford, Oxford, United Kingdom.
Benjamin
B
Irving
Department of Oncology, Aarhus University Hospital, Aarhus, Denmark.
Kari
K
Tanderup
Division of Imaging Science and Biomedical Engineering, King's College London, London, United Kingdom.
Julia A
JA
Schnabel
Institute of Biomedical Engineering, Department of Engineering Science University of Oxford, Oxford, United Kingdom.
Michael A
MA
Chappell
D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000378:metabolism*; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media
DCE-MRI; cervical cancer; linear least-squares method; nonlinear least-squares; pharmacokinetics; tracer kinetic modeling