Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI

Keil, Vera C.; Mädler, Burkhard; Gieseke, Jürgen; Fimmers, Rolf; Hattingen, Elke; Schild, Hans H.; Hadizadeh, Dariusch R.

doi:10.1016/j.mri.2017.04.006

Cited by 36 publications

(36 citation statements)

References 42 publications

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“…Our findings are in agreement with a recent study comparing fully automated and semiautomated AIF determination approaches for prostate DCE-MRI data analysis (7), showing that K trans variation owing to AIF uncertainty is the most prominent compared with other PK parameters. A similar conclusion was drawn in a brain DCE-MRI study (8) that investigated PK parameter variations caused by the use of AIFs measured from different vessels.…”

Section: Discussionsupporting

confidence: 74%

“…These observations are consistent with the results from a simulation study using the SSM (16), which found significantly lower sensitivity of k ep and τ i to a 30% change in AIF amplitude compared with K trans and v e . Interestingly, the aforementioned brain DCE-MRI study (8) using the extended Tofts model (18) also showed lower variation of k ep in response to different AIF sources compared with K trans . Because k ep , like K trans , is also a measure of perfusion and permeability, the low sensitivity of k ep to AIF amplitude uncertainty suggests that k ep could be a more robust and reproducible imaging biomarker than K trans for DCE-MRI characterization of tissue microvasculature (37) when consistent and accurate AIF quantification is difficult.…”

Section: Discussionmentioning

confidence: 95%

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The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II

et al. 2019

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This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate K trans (volume transfer rate constant), v e (extravascular, extracellular volume fraction), k ep (efflux rate constant), and τ i (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T 1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for K trans , v e , k ep , and τ i , respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in K trans and v e (wCV = 0.50 and 0.10, respectively), but had smaller effects on k ep and τ i (wCV = 0.39 and 0.22, respectively). k ep is less sensitive to AIF variation than K trans , suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τ i parameter may have advantages over the conventional PK parameters in a longitudinal study.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 95%

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II

et al. 2019

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“…Interpretations of DCE-MRI can involve qualitative analysis that requires computational-based curve-fitting algorithms using a bicompartmental model with AIF. However, one of its primary challenges is the reproducibility, as the quantification is heavily influenced by the definition of an appropriate AIF (52). Semiquantitative parameters seem to be more sensitive in the context of intratumoral heterogeneity, for example, to subtle physiologic differences that distinguish viable and hypoxic regions (48).…”

Section: Discussionmentioning

confidence: 99%

Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models

et al. 2019

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It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. Significance: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation.

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“…Although there are researches comparing ASL to positron emission tomography (PET) or microspheres for blood flow mapping, 33,34 it has not been validated against the ground truth blood flow directly since the ground truth in the real world is unknown. 1,35,36 And the gold standard microsphere deposition method or PET method suffer from many assumptions including the use of a global AIF. 37 The simulation in the current work based on the transport equation and microvascular network provides a ground truth for assessing quantification accuracy.…”

Section: Image Analysismentioning

confidence: 99%

Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network

Zhou

Zhang

Spincemaille

et al. 2020

Magnetic Resonance in Med

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Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI

Cited by 36 publications

References 42 publications

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II

Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models

Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network

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