In this work we aimed to study the possibility of using supervised classifiers to quantify the main components of carotid atherosclerotic plaque in vivo on the basis of multisequence MRI data. MRI data consisting of five MR weightings were obtained from 25 symptomatic subjects. Histological micrographs of endarterectomy specimens from the 25 carotids were used as a standard of reference for training and evaluation. The set of subjects was divided in a training set (12 subjects) and an evaluation set (13 subjects). Four different classifiers and two human MRI readers determined the percentages of calcified tissue, fibrous tissue, lipid core, and intraplaque hemorrhage on the subject level for all subjects in the evaluation set. Methods to assess the composition of atherosclerotic plaque have gained interest in the last decade because such findings may reveal the risk of heart disease and stroke (1-9). By quantifying plaque components in vivo, one can monitor the regression or progression of atherosclerosis. This would be important for the development of therapies to prevent thromboembolic events. Moreover, in vivo quantification of plaque components could be useful in making the decision to apply an existing therapy.In particular, plaques that contain lipid core and hemorrhage are considered to be at risk for thromboembolic events (1,4,5,8,9), but at present it is unclear which proportions of these high-risk components are clinically relevant (10 -15). Further studies are needed to determine the relationship between the proportion of high-risk components and thromboembolic events. Therefore, it is important to determine the accuracy and reproducibility of the methods used to quantify these components.It has been shown that multisequence MRI has the potential to detect the main plaque components both ex vivo (16,17) and in vivo (18 -23). Objective quantification of the plaque in terms of its main constituents on the basis of MR images is still an important problem. Therefore, recent studies have focused on developing (semi-)quantitative algorithms to characterize plaque (16,17,22). Several authors have shown the efficacy of ex vivo MRI in conjunction with algorithmic classifiers (16,17).Two essentially different classification methods are supervised and unsupervised classification (24). Supervised methods require training data (i.e., a set of input patterns with known classification). Given a new input pattern, a trained supervised method can predict its class.On the other hand, unsupervised classifiers simply separate the input data into a number of classes (clusters). In general, supervised classifiers are considered to be more powerful than unsupervised classifiers.In this study multisequence MRI was used for in vivo assessment of atherosclerotic plaque in the carotids of symptomatic subjects. Supervised classification algorithms were employed to quantify the main plaque components. Four plaque components were distinguished: calcified tissue, fibrous tissue, intraplaque hemorrhage, and lipid core. Results are a...
Magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) has been clinically shown to be effective for palliative pain management in patients suffering from skeletal metastasis. The underlying mechanism is supposed to be periosteal denervation caused by ablative temperatures reached through ultrasound heating of the cortex. The challenge is exact temperature control during sonication as MR-based thermometry approaches for bone tissue are currently not available. Thus, in contrast to the MR-HIFU ablation of soft tissue, a thermometry feedback to the HIFU is lacking, and the treatment of bone metastasis is entirely based on temperature information acquired in the soft tissue adjacent to the bone surface. However, heating of the adjacent tissue depends on the exact sonication protocol and requires extensive modelling to estimate the actual temperature of the cortex. Here we develop a computational model to calculate the spatial temperature evolution in bone and the adjacent tissue during sonication. First, a ray-tracing technique is used to compute the heat production in each spatial point serving as a source term for the second part, where the actual temperature is calculated as a function of space and time by solving the Pennes bio-heat equation. Importantly, our model includes shear waves that arise at the bone interface as well as all geometrical considerations of transducer and bone geometry. The model was compared with a theoretical approach based on the far field approximation and an MR-HIFU experiment using a bone phantom. Furthermore, we investigated the contribution of shear waves to the heat production and resulting temperatures in bone. The temperature evolution predicted by our model was in accordance with the far field approximation and agreed well with the experimental data obtained in phantoms. Our model allows the simulation of the HIFU treatments of bone metastasis in patients and can be extended to a planning tool prior to MR-HIFU treatments.
Cells of all organisms share the ability to respond to various extracellular signals. Depending on the cell type and the organism, these signals may include hormones secreted by other cells or changes in nutrient concentrations. The signals are processed by an intricate network of protein-protein interactions, including phosphorylation and de-phosphorylation events. As some signaling proteins are only present in low concentrations, random fluctuations may affect the dynamics of the network. The mathematical modeling of networks with significant random fluctuations requires the use of stochastic methods. The stochastic dynamics of a chemical reaction system are described by the Chemical Master Equation. Often the numerical evaluation of this equation is problematic. The first problem is that many systems have an infinite number of possible states; leaving simulations of individual trajectories as the only alternative. To circumvent this problem, we focus on a class of systems that have a finite state space. More specifically, we focus on networks of phosphorylation cycles without taking into account the synthesis and degradation of proteins. The second problem is that memory requirements cause a practical limit to the size of systems that can be evaluated. In this paper, we discuss how these limitations can be overcome using parallel computation and methods dealing efficiently with the available memory. These methods were implemented in a parallel C++ program. We discuss two networks for which the stochastic dynamics were evaluated using this program: a single phosphorylation cycle and an oscillating MAP-kinase cascade.
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