We present a multiple compartment, mammillary distributed-parameter model for capillary-tissue exchange, which can be implemented with dynamic contrast-enhanced imaging to study kinetic heterogeneity in tumors. The proposed n-compartment model consists of a vascular distributed-parameter compartment in direct exchange with a number (n - 1) of interstitial compartments. It is applied to a prostate tumor case study to illustrate the possible co-existence of two kinetically distinct compartments in the tumor, and the estimation of useful physiological parameters (such as perfusion, mean transit time, fractional volumes, and transfer and rate constants) associated with tissue microcirculation. The present model exhibits the convenient property of a separable impulse residue response function in time domain, which can be used to provide further insights and understanding on the physiological basis of tissue enhancement parameters commonly used for correlation studies with tumor histological diagnosis.
We present two regression models for the automatic estimation of bolus arrival times (BATs) in dynamic contrast MRI datasets. Results of Monte Carlo simulation experiments show that the means and standard deviations of the estimated BATs are within the sampling interval even in the presence of significant noise.
The assessment of tissue perfusion by dynamic contrast-enhanced (DCE) imaging involves a deconvolution process. For analysis of DCE imaging data, we implemented a regression approach to select appropriate regularization parameters for deconvolution using the standard and generalized singular value decomposition methods. Monte Carlo simulation experiments were carried out to study the performance and to compare with other existing methods used for deconvolution analysis of DCE imaging data. The present approach is found to be robust and reliable at the levels of noise commonly encountered in DCE imaging, and for different models of the underlying tissue vasculature. The advantages of the present method, as compared with previous methods, include its efficiency of computation, ability to achieve adequate regularization to reproduce less noisy solutions, and that it does not require prior knowledge of the noise condition. The proposed method is applied on actual patient study cases with brain tumors and ischemic stroke, to illustrate its applicability as a clinical tool for diagnosis and assessment of treatment response.
Dynamic contrast material-enhanced computed tomographic images of intracranial meningioma were analyzed by using both distributed-parameter and conventional compartmental tracer kinetic models. The distributed-parameter models were found to yield consistently better fitting of data sets than were conventional compartmental models. Although linear correlations were found between the kinetic parameters of the two models, some of these parameters (such as perfusion and mean transit time) did not correspond quantitatively. For all models, the kinetic parameters associated with the extravasation of tracer were found to be distinctly higher in meningiomas than in normal white- and gray-matter tissues.
This short communication presents significantly increased permeability in two patients with acute stroke, indicating an early blood-brain barrier disruption. Neither of the patients had undergone any thrombolytic therapy and hemorrhaged later. Increased permeability was assessed in both patients using a distributed-parameter model of capillary-tissue exchange. Our findings indicate that early physiologic imaging in stroke may identify patients with a high risk of hemorrhagic transformation by revealing pathologic vascular changes and, thus, guide therapeutic options.
Functional imaging has the potential to be a practical and widely-available method of studying the pathphysiology of disease using modern CT and MRI technologies. With the high temporal resolution achievable by these technologies, a two-compartment distributed-parameter model, which more accurate represents the tracer concentration within the vascular space, was applied on two patients' data with intracranial tumor and stroke. The parametric maps successfully generated were more informative than the current commercial software packages and the commonly used lumped-parameter compartmental models.
Dynamic contrast-enhanced (DCE) imaging is a functional imaging technique that can be used to assess tumor angiogenesis and yields quantitative estimates of tissue microcirculatory parameters by tracer kinetic model analysis. In this study, we explored the possible relationships between parameters of two tracer kinetic models, the generalized kinetic and distributed parameter models, by comparing these models on DCE CT datasets of actual patient study cases. Understanding the use of these models and the physiological meaning of their parameters would improve the interpretation and assessment of anti-angiogenic drug trials by DCE imaging.
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