Dynamic Contrast-enhanced CT of Intracranial Meningioma: Comparison of Distributed and Compartmental Tracer Kinetic Models—Initial Results

Cheong, L.H.; Lim, C. C. Tchoyoson; Koh, Tong San

doi:10.1148/radiol.2323031198

Cited by 47 publications

(22 citation statements)

References 38 publications

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“…Two compartment models of this kind have been used to interpret data from dynamic CT (Brix et al 1999; Cheong et al 2004), MRI (Sourbron et al 2009; Larsson et al 2009; Brix et al 2004), and PET (Larson et al 1987). …”

Section: Methodsmentioning

confidence: 99%

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

et al. 2014

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Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. Methods DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml/(min*g), cardiac output = 3, 5, 8 L/min). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiologic features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 sec sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11,000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. Results After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. Conclusions MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that there is no particular advantage between quantitative estimation methods nor to performing dose reduction via tube current reduction compared to temporal sampling reduction. These data are important for optimizing implementation of cardiac dynamic CT in clinical practice and in prospective CT MBF trials.

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Section: Methodsmentioning

confidence: 99%

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

et al. 2014

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“…193 The 2CXM model 6,127,194 and the more general multicompartment models 135,195,[197][198][199] resolve the ambiguity in interpreting the K trans -estimates from the TK and ETK models. This is a well-known model in classical pharmacokinetics 320 and has been applied to analyze nuclear medicine data by Larson et al 126 and adopted for the perfusion analysis by Brix et al 321,322 and Cheong et al 323 Classification of breast tumors by Brix et al 6 was its first DCE-MRI application. Recently, the 2CXM is gradually becoming common in various applications, such as brain 1,2,194 and lung cancer, 324 myometrium, 325 cervix 127 and bladder cancer, 326 head and neck tumors, 327 and carotid atherosclerotic plaques.…”

Section: E Clinical Applications Of Parametric Modelsmentioning

confidence: 99%

Models and methods for analyzing DCE‐MRI: A review

Khalifa¹,

Soliman²,

El-Baz³

et al. 2014

Medical Physics

254

230

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“…The better performances of ATH were in part expected considering that this model is in principle more realistic and that C p (t) has been derived from real data. However, the improvement in terms of goodness-of-fit was not decisive, probably because of several reasons: first, the temporal resolution achievable in current clinical settings could be not yet sufficient to satisfy the requirements on the capillary mean transit time; second, the typical signal-to-noise ratio achievable in current clinical setting could be not adequate to discriminate finer features of the CA time-course; finally, a major limitation of this study was the small number of subjects involved: this fact implies that the range of physiological parameters explored was limited, although they should be representative of important types of carcinomas, however it is worth to note that also in previous studies reporting comparison between models the number of subjects was small [28] .…”

Section: Discussionmentioning

confidence: 92%

Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models

Fusco

Sansone²,

Maffei³

et al. 2012

JBGC

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Background/objectives: Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used in tumor diagnosis, staging and assessment of therapy response for different types of tumors, thanks to its capability to provide important functional information about tissue microvasculature. Tracer kinetic models used for estimating microcirculatory parameters can be broadly categorized as conventional compartmental (CC) or distributed- parameter (DP) models. While DP models seem to be more realistic, CC models (in particular the Tofts and the Brix models) have been widely used in clinical investigations over the past two decades. However, to date there is no direct comparison of CC vs DP models on real breast DCE-MRI data; moreover, a direct comparison between Tofts and Brix models, has not yet been reported on real breast data. Therefore, the purpose of this study was two-fold: on the one hand we analyzed the performance, on real breast DCE-MRI data, of CC vs DP models in terms of goodness-of-fit metrics; on the other hand we compared Tofts and Brix models on the basis of real breast DCE-MRI data. Methods: Three models were compared: two CC models (the Tofts and the Brix models) and one DP model (the ATH model). We gathered data in two different scenarios: DCE-MRI with high temporal resolution obtained by means of a k-space under-sampling and data sharing method known as Time-resolved angiography With Stochastic Trajectories (TWIST) and DCE-MRI with low temporal resolution obtained by means of the Spoiled Gradient-Echo k-space scheme known as Fast Low Angle Shot (FLASH). The performances of the three models were evaluated by means of three goodness-of-fit metrics: the Residual Sum of Squares, the Bayesian Information Criterion and the Akaike Information Criterion on four breast DCE-MRI examinations. Results: Although not conclusive, the results of this study suggest that the ATH model can achieve better fit in comparison to the Tofts and Brix models for TWIST data; and that the Brix model can achieve better fit with respect to the Tofts model for FLASH data. Conclusion: Given the current typical settings of clinical breast DCE-MRI examinations, there seems not to be a clear advantage, in terms of goodness-of-fit, of ATH with respect to Tofts and Brix models; moreover, at lower temporal resolution the Brix model can achieve better fit than the Tofts model

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Dynamic Contrast-enhanced CT of Intracranial Meningioma: Comparison of Distributed and Compartmental Tracer Kinetic Models—Initial Results

Cited by 47 publications

References 38 publications

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

Models and methods for analyzing DCE‐MRI: A review

Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models

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