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.

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.

Independent component analysis of dynamic contrast-enhanced computed tomography images

Independent component analysis (ICA) was applied on dynamic contrast-enhanced computed tomography images of cerebral tumours to extract spatial component maps of the underlying vascular structures, which correspond to different haemodynamic phases as depicted by the passage of the contrast medium. The locations of arteries, veins and tumours can be separately identified on these spatial component maps. As the contrast enhancement behaviour of the cerebral tumour differs from the normal tissues, ICA yields a tumour component map that reveals the location and extent of the tumour. Tumour outlines can be generated using the tumour component maps, with relatively simple segmentation methods.

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.

On the Selection of Optimal Flip Angles for$T_1$Mapping of Breast Tumors With Dynamic Contrast-Enhanced Magnetic Resonance Imaging

We present a method for selecting optimal flip angles for both precontrast and postcontrast T1 mapping of breast tumors using dynamic contrast-enhanced magnetic resonance imaging; and with the aim of improving accuracy in the estimation of contrast medium concentration. The proposed method can appropriately account for the different ranges of precontrst and postcontrast T1 values by the use of weighting functions, which also allow the flexibility to enhance the accuracy of certain T1 values, corresponding to the tissues of interest. Results of Monte Carlo simulations show that the proposed method could yield significantly lower errors in the estimation of contrast concentration, as compared with an existing approach.

A biphasic parameter estimation method for quantitative analysis of dynamic renal scintigraphic data

Dynamic renal scintigraphy is an established method in nuclear medicine, commonly used for the assessment of renal function. In this paper, a biphasic model fitting method is proposed for simultaneous estimation of both vascular and parenchymal parameters from renal scintigraphic data. These parameters include the renal plasma flow, vascular and parenchymal mean transit times, and the glomerular extraction rate. Monte Carlo simulation was used to evaluate the stability and confidence of the parameter estimates obtained by the proposed biphasic method, before applying the method on actual patient study cases to compare with the conventional fitting approach and other established renal indices. The various parameter estimates obtained using the proposed method were found to be consistent with the respective pathologies of the study cases. The renal plasma flow and extraction rate estimated by the proposed method were in good agreement with those previously obtained using dynamic computed tomography and magnetic resonance imaging.

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.

Fabrication of Size-Tunable Gold Nanoparticles Array with Nanosphere Lithography, Reactive Ion Etching, and Thermal Annealing

Two-dimensional ordered arrays of gold (Au) nanoparticles were fabricated using two different variants of the nanosphere lithography technique. First, ordered arrays of polystyrene nanospheres on Si substrate were used as deposition masks through which gold films were deposited by electron beam evaporation. After the removal of the nanospheres, an array of triangular Au nanodisks was left on the Si substrate. After thermal annealing at increasing temperature, systematic shape transition of the nanostructures from original triangular Au nanodisks to rounded nanoparticles was observed. This approach allows us to systematically vary the size and morphology of the particles. In the second and novel technique, we made use of reactive ion etching to simultaneously reduce the dimension of the masking nanospheres and create arrays of nanopores on the substrate prior to the deposition of the Au films. These samples were subsequently annealed, which resulted in size-tunable and ordered Au nanoparticle arrays with the nanoparticles nested in the nanopores of the templated substrate. With the nanoparticles anchored in the nanopores, the substrate could be useful as a template for growth of other nanomaterials.

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.

Estimation of renal scintigraphy parameters using a linear piecewise-continuous model

Instead of performing a numerical deconvolution, we propose to use a linear piecewise-continuous model of the renal impulse response function for parametric fitting of renal scintigraphy data, to obtain clinically useful renal parameters. The strengths of the present model are its simplicity and speed of computation, while not compromising on accuracy. Preliminary patient case studies show that the estimated parameters are in good agreement with a more elaborate model.

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.

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.

A numerical method for estimating blood flow by dynamic functional imaging

We present a numerical deconvolution scheme for estimating regional blood flow and tissue retention functions by dynamic functional imaging. The present approach implements the Tikhonov-Miller regularization in general form, which allows for prior knowledge or assumptions to be incorporated during the deconvolution process, so as to stabilize the solution against variations due to noise. Appropriate approximations and simplifications in the context of functional imaging, were also introduced to ease numerical computations. Monte Carlo simulation experiments were carried out to study the applicability of the present approach and to compare with other deconvolution techniques previously studied.

A practical approach to inverse planning for high-precision dose escalated conformal prostate radiotherapy

The problem of choosing the best gantry angles and beam weights for dose-escalated conformal prostate treatment planning is formulated using a mixed-integer linear programming approach, to account for tumour dose homogeneity and dose-volume constraints. The formulation allows the number of beams to be restricted and for some of the beams to be compulsory. The present planning algorithm interfaces with and utilizes the three-dimensional planning capabilities of a commercial treatment planning system. A case study is illustrated, which represents a particularly challenging planning problem due to a large planning target volume and an unusually small bladder. Treatment plans with different numbers of beams are generated to compare with each other and with the standard six-field plan. Significant improvement is shown in the reduction of hot regions within the femoral heads and rectal wall, while not unduly compromising homogeneity constraints for the tumour.

The inclusion of capillary distribution in the adiabatic tissue homogeneity model of blood flow

We have developed a non-invasive imaging tracer kinetic model for blood flow which takes into account the distribution of capillaries in tissue. Each individual capillary is assumed to follow the adiabatic tissue homogeneity model. The main strength of our new model is in its ability to quantify the functional distribution of capillaries by the standard deviation in the time taken by blood to pass through the tissue. We have applied our model to the human prostate and have tested two different types of distribution functions. Both distribution functions yielded very similar predictions for the various model parameters, and in particular for the standard deviation in transit time. Our motivation for developing this model is the fact that the capillary distribution in cancerous tissue is drastically different from in normal tissue. We believe that there is great potential for our model to be used as a prognostic tool in cancer treatment. For example, an accurate knowledge of the distribution in transit times might result in an accurate estimate of the degree of tumour hypoxia, which is crucial to the success of radiation therapy.

An automated approach to seed assignment for eye plaque brachytherapy

Episcleral plaques are commonly used for the treatment of ocular tumours such as choroidal melanoma. Treatment planning involves the assignment of seeds to slots on the plaque to achieve a desired dose rate distribution. Seed assignment is rather straightforward if seeds are ordered on demand. However, the assignment task becomes tedious and laborious if the seeds have to be chosen from an existing stock of seeds with different activities. To date, this task has usually been performed by a human planner through trial and error. An algorithm has been developed to automate the task of seed assignment using a mixed-integer programming method. We also explore ways to simplify the problem such that the method becomes practical in most facilities. We have tested the method on three randomly chosen clinical cases from our past records, to show that the algorithm could yield solutions within a shorter time frame and with less deviation from the desired dose rate distributions, as compared with the solutions from a human counterpart.

Dietary copper level affects copper metabolism during lipopolysaccharide-induced immunological stress in chicks

Two experiments were conducted to examine the effect of dietary Cu level on Cu metabolism during the acute phase response in broiler chicks with adequate (Experiment 1) or deficient (Experiment 2) Cu. Diets based on cornstarch and isolated soybean protein were used to formulate a basal diet, and basal diet plus either 5, 10, or 15 mg/kg additional Cu as either CuO or CuSO4. Each diet was fed to six pens of five chicks per pen (Experiment 1) or eight pens of five chicks (Experiment 2). Half of the chicks on each diet were injected with Salmonella typhymurium lipopolysaccharide (LPS) on alternate days. In Experiment 1, LPS significantly decreased daily gain, feed intake, and feed efficiency (P < 0.01) and increased the concentration of Cu in blood plasma (P < 0.01). In the uninjected birds, adding 5, 10, or 15 mg/kg Cu as CuO or 15 mg/kg Cu as CuSO4 increased the rate of gain over that of chicks fed the basal diet. In the birds challenged with LPS, 10 mg/kg Cu as CuO increased the rate of gain and efficiency compared to those of chicks fed the basal diet. Addition of CuSO4 to the diet of chicks challenged with LPS did not affect gain, intake, or feed efficiency compared to those of chicks fed the basal diet. Ceruloplasmin levels were higher in chicks challenged with LPS than in control chicks (P = 0.03), and this difference tended to be greater in chickens fed CuO than in chickens fed CuSO4 (P = 0.07). In chicks challenged with LPS, feeding CuO at all levels and feeding CuSO4 to give 10 or 15 mg/kg Cu increased ceruloplasmin levels above that of chicks fed the basal diet. Hepatic Mn superoxide dismutase (SOD) and Cu/Zn SOD were not influenced by dietary Cu level or source or LPS. Results of Experiment 2 were similar to those of Experiment 1 except that supplemental CuSO4 and CuO gave similar increases in gain and CuSO4 was more effective at increasing ceruloplasmin levels. Chicks given supplemental Cu had higher ceruloplasmin levels following challenge with LPS than Cu-deficient chicks fed the basal diet. Apparently, Cu requirements are higher for chicks experiencing an acute phase response than for healthy chicks.

Synthesis and Structure of N,N'N"-Tris(2-hydroxyethyl)-N,N,N',N',N',N'-hexamethyl-2,2',2'-nitrilotris(ethylammonium) Trichloride

The synthesis of N,N′,N″- tris(2-hydroxyethyl)-N,N,N′,N′,N″,N″-hexamethyl-2,2′,2″-nitrilotris( ethylammonium ) trichloride, a potential monomer for the preparation of polyurethane-based ion exchangers, is described and its structure reported based on X-ray diffraction data.