Detection of early vessel leakiness in acute ischemic stroke using computed tomography perfusion may indicate hemorrhagic transformation.

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.

Prediction of subsequent hemorrhage in acute ischemic stroke using permeability CT imaging and a distributed parameter tracer kinetic model.

PURPOSE: Hemorrhagic transformation (HT) is a common consequence of infarction independent of thrombolytic therapy. Our purpose was to examine if permeability imaging in admission perfusion CT data of patients with acute stroke might indicate a subsequent HT by imaging the disrupted permeability barriers between blood and brain. MATERIALS AND METHODS: A distributed parameter model analysis of the perfusion data were used to analyze the admission perfusion surveys of eight patients with HT of the initial infarct without thrombolysis. The perfusion findings of these patients were compared with those of eight age- and gender-matched patients from the initial group that did not present with HT. RESULTS: The applied statistics for comparing the ischemic voxels with the contralateral healthy tissue showed significantly higher permeability-surface product (PS), extraction ratio (E), and extracellular extravascular space volume (V(EES)) in the ischemic voxels (P range, 0.05-0.0001). In the patients without HT, the PS, E and V(EES) values in the ischemic voxels were not significantly different from those in the normal region. CONCLUSION: Our findings indicate that early perfusion CT physiological imaging in stroke is a promising tool for identifying patients with higher risk of HT and, thus, may serve to guide therapeutic options.

Cerebral perfusion mapping using a robust and efficient method for deconvolution analysis of dynamic contrast-enhanced images.

Dynamic contrast-enhanced (DCE) imaging using MRI or CT is emerging as a promising tool for diagnostic imaging of cerebral disorders and the monitoring of tumor response to treatment. In this study, we present a robust and efficient deconvolution method based on a linearized model of the impulse residue function, which allows for the mapping of functional cerebral parameters such as cerebral blood flow, volume, mean transit time, and permeability. Monte Carlo simulation studies were performed to study the accuracy and stability of the proposed method, before applying it to clinical study cases of patients with cerebral tumors imaged using DCE CT. Functional parameter maps generated using the proposed method revealed the locations of the cerebral tumors and were found to be of sufficiently good clarity for marked regional differences in tissue vascularity and permeability to be assessed. In particular, tumor visualization and delineation were found to be better on the parameter maps that were indicative of the breakdown of the blood-brain barrier.

A distributed parameter model of cerebral blood-tissue exchange with account of capillary transit time distribution.

Quantitative estimates of physiological parameters associated with cerebral blood flow can be derived from the analysis of dynamic contrast-enhanced (DCE) images, using an appropriate model of the underlying tissue impulse residue function. The theoretical formulation of a distributed parameter model of tissue microcirculation, which accounts for the effects of capillary permeability and transit time distribution, is presented here. This model considers a statistical distribution of capillary-tissue units, each described by a distributed parameter model that accounts for convective transport within the capillary and transcapillary axial diffusion. Monte Carlo simulations were performed to study the confidence of the parameter estimates, and the model was used to analyze DCE CT images of patient study cases with metastatic cerebral tumors. The tumors were found to yield significantly higher estimates than normal tissues for the parameters associated with the extravasation of tracer and for the standard deviation of capillary transit times. The proposed model can be used with DCE imaging to study the microcirculatory characteristics of cerebral tumors.

Functional Imaging: Dynamic Contrast-Enhanced CT using a Distributed-Parameter Physiologic Model for Accessing Stroke and Intracranial Tumor.

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.

Assessment of perfusion by dynamic contrast-enhanced imaging using a deconvolution approach based on regression and singular value decomposition.

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-enhanced CT of intracranial meningioma: comparison of distributed and compartmental tracer kinetic models--initial results.

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.

A physiologic model of capillary-tissue exchange for dynamic contrast-enhanced imaging of tumor microcirculation.

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.

An automatic approach for estimating bolus arrival time in dynamic contrast MRI using piecewise continuous regression models.

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.

Laparoscopic marsupialization of liver cysts.

The majority of benign, non-parasitic liver cysts are asymptomatic. Surgical treatment is reserved for symptomatic patients and frequently involves partial excision or marsupialization via a laparotomy. Surgery is occasionally offered for asymptomatic large cysts, where complications of cyst rupture, intra-cystic bleeding and infection are more common. The authors describe three cases of symptomatic liver cysts that were effectively marsupialized laparoscopically with good results.