Dynamic Contrast-Enhanced MR: Importance of Reaching the Washout Phase

Letters to the Editor Letters to the Editor

Published online before print March 28, 2013, doi: 10.3174/ajnr.A3556
AJNR 2013 34: E58-E59

P. Alcaide-Leona
aMR Unit
Department of Radiology
Passeig de la Vall d’Hebron
Barcelona, Spain

À. Rovirab
bHospital Universitari Vall d’Hebron
Barcelona, Spain

We read with great interest the recent study published in the American Journal of Neuroradiology on November 22, 2012, entitled “T1-Weighted Dynamic Contrast-Enhanced MR Evaluation of Different Stages of Neurocysticercosis and Its Relationship with Serum MMP-9 Expression.”1

In the Materials and Methods section, the authors describe their dynamic contrast-enhanced (DCE) study protocol as follows: “A series of 384 images during 32 time points for 12 sections were acquired (temporal resolution, 5.65 seconds).” However, 32 time points × 5.65 seconds of temporal resolution results in a 3-minute-long DCE-MR study. The specific pharmacokinetic model and the postprocessing software used for calculation of the transfer constant (Ktrans); rate constant between extracellular extravascular space and blood plasma (Kep); and leakage space (Ve) are not stated in the article.

We have been using DCE-MR imaging as a part of our tumor protocol since 2010. We usually perform a 4-minute DCE study. With a study of this length, the washout phase is not usually reached in brain tumors. Inflammatory lesions such us neurocysticercosis are known to wash out much later than tumors as the result of their low permeability surface area product (PS).

In their article published in 1991, Tofts and Kermode2 generated families of tracer concentration in tissue [Ct(t)] curves from their equation and demonstrated that for fixed permeability, increasing Ve has no effect on the initial slope but does affect the maximum concentration reached and delays the time to peak enhancement. From this assessment, we infer that reaching the washout phase is needed to obtain Ve and Kep.

To confirm our hypothesis, we performed a 14-minute DCE study in a patient with pilocytic astrocytoma. Maximum enhancement is reached at 5.8 minutes, and a slow washout curve then begins. We performed 2 different Kep calculations: the first one by use of the first 3 minutes of the DCE study and the second one by use of the whole 15-minute sequence. Resulting Kep values are as different as Kep (3-minute DCE): 0.50 minutes−1 and Kep (14-minute DCE): 0.13 minutes−1. T1 kinetic analysis was based on the 2-compartment extended pharmacokinetic model of Tofts and Kermode by use of nordicICE software (NordicImagingLab, Bergen, Norway) (Figs 1 and 2).

  • Fig 1.

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  • Fig 1. Kep map obtained at 14 minutes in a cerebellar pilocytic astrocytoma (left image). Green curverepresents the time–signal intensity curve of a region of interest (red circle in left image); red curve represents measured arterial input function; blue line corresponds to the last image considered for calculation (14 minutes) (right image). Kep value calculated for the whole 14-minute DCE study was 0.13 minutes−1.
  • Fig 2. 
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  • Fig 2. Kep map obtained at 3 minutes in a cerebellar pilocytic astrocytoma (left image). Green curve represents the time–signal intensity curve of a region of interest (red circle in left image); red curverepresents measured arterial input function; blue line corresponds to the last image considered for calculation (3 minutes) (right image). At 3 minutes, the washout phase has not been reached and Kep value in this region of interest was 0.50 minutes−1.

To sum up, the effect of not collecting for long enough to reach the washout phase would be that Ve and Kep would be imprecise (ie, poor repeatability), and the performance of those biomarkers would be compromised. Available DCE software provides Kep and Ve maps even without the data needed to support those calculations, and we should be aware of this fact.

Acknowledgments

We thank Professor Paul Tofts for his help.

References

  1. Gupta RK, Awasthi R, Garg RK, et al. T1-weighted dynamic contrast-enhanced MR evaluation of different stages of neurocysticercosis and its relationship with serum MMP-9 expression. AJNR Am J Neuroradiol 2012 November 22, 2012 [Epub ahead of print] » Search Google Scholar
  2. Tofts PS, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging, 1: fundamental concepts. Magn Reson Med 1991;17:357–67 » Medline

Reply

Published online before print March 28, 2013, doi: 10.3174/ajnr.A3578
AJNR 2013 34: E60

R.K.S. Rathorea
aDepartment of Mathematics and Statistics
Indian Institute of Technology, Kanpur, India

R.K. Guptab
bDepartment of Radiology and Imaging
Fortis Memorial Research Institute
Gurgaon, India

We thank Paula Alcaide-Leon and Álex Rovira for their interest in our work.1 In a given a time-series model involving a number of parameters, it is reasonable to expect that a determination of the parameters by using very few time points may result in erroneous estimates due to noise in the data. However, if one uses enough time points, the computations are expected to be relatively error-free; the inclusion of too many time points need not result in any better estimates.

Furthermore, if the model describes the data (ie, it is applicable), the variability in the parameter estimates using variable time points should only be random. A systematic variation in estimated parameter with respect to variable time points is indicative of an inadequacy of the model to describe the data.

The generalized tracer kinetic model (GTKM) given by
Formulais a 2-compartment model used by the commenting authors.

To resolve the persistence of uptake, researchers are using a model that assumes unidirectional exchange (ie, from the capillary plasma to the extracellular extravascular space [EES]2⇓–4), which essentially consists of the Patlak model:
Formuladescribing a pure contrast uptake voxel.

The actual situation, however, appears to be best described by the 3-compartment leaky tracer kinetic model (LTKM)5:
Formulapresented in Rathore et al6,7 and Sahoo et al.8

LTKM reduces to GTKM if the leakage space is absent (λtr = 0); and it reduces to the Patlak model in the absence of a permeable space (ktr = 0).

The systematic variability of the tracer kinetic parameters using GTKM and its cessation using LTKM is considered at length in Sahoo et al.5

In short, it not necessary to prolong the observations until the washout phase, and a study of approximately 3 minutes is quite adequate. What is needed is to use the correct model in which the constancy of the parameters is restored to its original state. For further discussion of LTKM, readers may refer to Sahoo et al.5

We used only an in-house-developed code for our computations.

References

  1. Gupta RK, Awasthi R, Garg RK, et al. T1-weighted dynamic contrast-enhanced MR evaluation of different stages of neurocysticercosis and its relationship with serum MMP-9 expression. AJNR Am J Neuroradiol 2012 Nov22. [Epub ahead of print] » Search Google Scholar
  2. Sourbron SP, Buckley DL. Tracer kinetic modeling in MRI: estimating perfusion and capillary permeability. Phys Med Biol 2012;57:R1–33 » CrossRef » Medline
  3. Li KL, Zhu XP, Checkley DR, et al. Simultaneous mapping of blood volume and endothelial permeability surface area product in gliomas using iterative analysis of first-pass dynamic contrast enhanced MRI data. Br J Radiol 2003;76:39–50 » Abstract/FREE Full Text
  4. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab 1983;3:1–7 » Medline
  5. Sahoo P, Rathore RK, Awasthi R, et al. Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high- and low-grade glioma using dynamic contrast-enhanced (DCE) MRI. J Magn Reson Imaging 2013 Feb 6. [Epub ahead of print] » Search Google Scholar
  6. Rathore RKS, Sahoo P, Awasthi R,et al. A modified generalized tracer kinetic model for perfusion parameters in DCE-MRI for high grade intracranial mass lesions. In: Proceedings of the Nineteenth Annual Meeting of the International Society of Magnetic Resonance in Medicine, Montreal, Quebec, Canada; May 6–13, 2011 » Search Google Scholar
  7. Rathore RKS, Gupta RK, Sahoo P, et al. DCE-MRI using a three compartment leaky tracer kinetic model (LTKM) for whole body applications. In: Proceedings of the Twentieth Annual Meeting of the International Society of Magnetic Resonance in Medicine, Melbourne, Australia; May 5–11, 2012 » Search Google Scholar
  8. Sahoo P, Awasthi R, Rathore RKS, et al. Effects of AIF selection and pharmacokinetic model selection on discrimination of chronic infective from chronic inflammatory knee arthritis using DCE-MRI. In: Proceedings of the Twentieth Annual Meeting of the International Society of Magnetic Resonance in Medicine. Melbourne, Australia; May 5–11, 2012 » Search Google Scholar
Dynamic Contrast-Enhanced MR: Importance of Reaching the Washout Phase