Operator dependency of arterial input function in dynamic contrast-enhanced MRI

Magne Kleppestø; Atle Bjørnerud; Inge Rasmus Groote; Minjae Kim; Jonas Vardal; Christopher Larsson

doi:10.1007/s10334-021-00926-z

Operator dependency of arterial input function in dynamic contrast-enhanced MRI

MAGMA. 2022 Feb;35(1):105-112. doi: 10.1007/s10334-021-00926-z. Epub 2021 Jul 2.

Authors

Magne Kleppestø^{1

2

3}, Atle Bjørnerud^{4

5

6}, Inge Rasmus Groote^{4

7}, Minjae Kim^{8

9}, Jonas Vardal^{4

10}, Christopher Larsson¹¹

Affiliations

¹ Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway. mamkle@ous-hf.no.
² Faculty of Medicine, University of Oslo, Oslo, Norway. mamkle@ous-hf.no.
³ Unit for Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway. mamkle@ous-hf.no.
⁴ Unit for Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
⁵ Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
⁶ Department of Psychology, Faculty for Social Sciences, University of Oslo, Oslo, Norway.
⁷ Department of Radiology, Vestfold Hospital Trust, Tønsberg, Norway.
⁸ Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
⁹ Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
¹⁰ Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway.
¹¹ Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway.

Abstract

Objective: To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas.

Methods: The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in K^trans was assessed by Fleiss' kappa (κ).

Results: There was a moderate to substantial agreement (ICC 0.51-0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77-0.95 for K^trans, k_ep and v_e and ICC 0.70 for v_p. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in K^trans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement.

Discussion: AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

Keywords: AIF; Arterial input function; DCE-MRI; Dynamic contrast-enhanced MRI; Glioblastoma; Observer dependency.

MeSH terms

Algorithms
Arteries / diagnostic imaging
Contrast Media* / pharmacokinetics
Humans
Magnetic Resonance Imaging* / methods
Reproducibility of Results

Substances

Contrast Media