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A simulation study comparing nine mathematical models of arterial input function for dynamic contrast enhanced MRI to the Parker model

  • Dianning He
  • Lisheng Xu
  • Wei Qian
  • James Clarke
  • Xiaobing Fan
Scientific Note
  • 117 Downloads

Abstract

Due to large inter- and intra-patient variabilities of arterial input functions (AIFs), accurately modeling and using patient-specific AIF are very important for quantitative analysis of dynamic contrast enhanced MRI. Computer simulations were performed to evaluate and compare nine population AIF models with the Parker AIF used as ‘gold standard’. The Parker AIF was calculated with a temporal resolution of 1.5 s, and then the other nine AIF models were used to fit the Parker AIF. A total of 100 randomly generated volume transfer constants (Ktrans) and distribution volumes (ve) were used to calculate the contrast agent concentration curves based on the Parker AIF and the extended Tofts model with blood plasma volume (vp) = 0.0, 0.01, 0.05 and 0.10. Subsequently, nine AIF models were used to fit these curves to extract physiological parameters (Ktrans, ve and vp). The agreements between generated and extracted Ktrans and ve values were evaluated using Bland–Altman analysis. The effects of the second pass of the Parker AIF model with and without adding Rician noise on extracted physiological parameters were evaluated by 1000 simulations using one of the nine mathematical AIF models closest to the Parker model with the smallest number of parameters. The results demonstrated that a six-parameter linear function plus bi-exponential function AIF model was almost equivalent to the Parker AIF and that the corresponding generated and extracted Ktrans and ve were in excellent agreements. The effects of the second pass of contrast agent circulation were small on extracted physiological parameters using the extended Tofts model, unless noise was added with signal to noise ratio less than 10 dB.

Keywords

Dynamic contrast enhanced MRI Pharmacokinetic model Arterial input functions Computer simulations Contrast agent concentration curves 

Notes

Acknowledgements

This work was supported by the University of Chicago Cancer Center, the National Natural Science Foundation of China under Grant (No. 61672146).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human and animal participants

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

13246_2018_632_MOESM1_ESM.pdf (2.6 mb)
Supplementary material 1 (PDF 2661 KB)

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  • Dianning He
    • 1
    • 2
  • Lisheng Xu
    • 1
    • 3
  • Wei Qian
    • 1
    • 4
  • James Clarke
    • 2
  • Xiaobing Fan
    • 2
  1. 1.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangChina
  2. 2.Department of RadiologyThe University of ChicagoChicagoUSA
  3. 3.Key Laboratory of Medical Image ComputingMinistry of EducationShenyangChina
  4. 4.Electrical and Computer EngineeringUniversity of Texas at El PasoEl PasoUSA

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