Literature DB >> 29569210

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

Dianning He1,2, Lisheng Xu1,3, Wei Qian1,4, James Clarke2, Xiaobing Fan5.   

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.

Entities:  

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

Mesh:

Substances:

Year:  2018        PMID: 29569210     DOI: 10.1007/s13246-018-0632-0

Source DB:  PubMed          Journal:  Australas Phys Eng Sci Med        ISSN: 0158-9938            Impact factor:   1.430


  3 in total

1.  Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters.

Authors:  Yousef Mazaheri; Nathanael Kim; Yulia Lakhman; Ramin Jafari; Alberto Vargas; Ricardo Otazo
Journal:  NMR Biomed       Date:  2022-03-14       Impact factor: 4.478

2.  Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI.

Authors:  Daniel Lewis; Xiaoping Zhu; David J Coope; Sha Zhao; Andrew T King; Timothy Cootes; Alan Jackson; Ka-Loh Li
Journal:  Sci Rep       Date:  2022-05-24       Impact factor: 4.996

3.  Simultaneously spatial and temporal Higher-Order Total Variations for noise suppression and motion reduction in DCE and IVIM.

Authors:  Renjie He; Yao Ding; Abdallah S R Mohamed; Sweet Ping Ng; Rachel B Ger; Hesham Elhalawani; Baher A Elgohari; Kristina H Young; Katherine A Hutcheson; Clifton D Fuller; Stephen Y Lai
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-03-10
  3 in total

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