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Background and Objective: State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid region-based energy formulation for effective segmentation in computed tomography angiography (CTA) imagery., Methods: The proposed hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The segmentation is achieved using a level set formulation due to the computational robustness. After validating the statistical significance of the hybrid energy, we applied the proposed model to solve an important clinical problem of 3D coronary segmentation. An improved seed detection method is used to initialize the level set evolution. Moreover, we employed an auto-correction feature that captures the emerging peripheries during the curve evolution for completeness of the coronary tree., Results: We evaluated the segmentation accuracy of the proposed energy model against the existing techniques in two stages. Qualitative and quantitative results demonstrate the effectiveness of the proposed framework with a consistent mean sensitivity and specificity measures of 80% across the CTA data. Moreover, a high degree of agreement with respect to the inter-observer differences justifies the generalization of the proposed method., Conclusions: The proposed method is effective to segment the coronary tree from the CTA volume based on hybrid image based energy, which can improve the clinicians ability to detect arterial abnormalities., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Coronary artery disease (CAD) is the most common type of heart disease in western countries. Early detection and diagnosis of CAD is quintessential to preventing mortality and subsequent complications. We believe hemodynamic data derived from patient-specific computational models could facilitate more accurate prediction of the risk of atherosclerosis. We introduce a semiautomated method to build 3D patient-specific coronary vessel models from 2D monoplane angiogram images. The main contribution of the method is a robust segmentation approach using dynamic programming combined with iterative 3D reconstruction to build 3D mesh models of the coronary vessels. Results indicate the accuracy and robustness of the proposed pipeline. In conclusion, patient-specific modelling of coronary vessels is of vital importance for developing accurate computational flow models and studying the hemodynamic effects of the presence of plaques on the arterial walls, resulting in lumen stenoses, as well as variations in the angulations of the coronary arteries.

Studies evaluating the diagnostic performance of coronary computed tomography angiography (CTA) are consistent in demonstrating a high negative predictive accuracy, but only a modest positive predictive accuracy for the detection of significant coronary artery disease. Consequentially, there has been a considerable effort made to enhance the diagnostic capability of coronary CTA by developing scanner technology and also post-processing algorithms for coronary stenosis evaluation. Of these new developments, the proposition of being able to measure non-invasive fractional flow reserve by coronary computed tomography angiography (FFRct) has generated much recent interest. Initial reports indicate that the application FFRct not only correlates well with invasive fractional flow reserve but also has the potential to enhance substantially the positive predictive accuracy and overall accuracy of coronary CTA. Although it is theoretically possible to measure FFRct using complex computational fluid dynamics adapted from the aeronautical industry, this approach is likely to face a number of challenges prior to it being accepted into the mainstream as an adjunct to coronary CTA. The aim of the current review is to provide an overview of: 1) the fundamental engineering principles behind computational fluid dynamic modelling of coronary arterial blood flow; 2) the difficulties faced from an engineering perspective in developing a truly representative model; and 3) the challenges this technology is likely to face as it attempts to enter the clinical domain.

Reliable and reproducible estimation of vessel centerlines and reference surfaces is an important step for the assessment of luminal lesions. Conventional methods are commonly developed for quantitative analysis of the "straight" vessel segments and have limitations in defining the precise location of the centerline and the reference lumen surface for both the main vessel and the side branches in the vicinity of bifurcations. To address this, we propose the estimation of the centerline and the reference surface through the registration of an elliptical cross-sectional tube to the desired constituent vessel in each major bifurcation of the arterial tree. The proposed method works directly on the mesh domain, thus alleviating the need for image upsampling, usually required in conventional volume domain approaches. We demonstrate the efficiency and accuracy of the method on both synthetic images and coronary CT angiograms. Experimental results show that the new method is capable of estimating vessel centerlines and reference surfaces with a high degree of agreement to those obtained through manual delineation. The centerline errors are reduced by an average of 62.3% in the regions of the bifurcations, when compared to the results of the initial solution obtained through the use of mesh contraction method.

In this paper, we present a novel two-step algorithm for segmentation of coronary arteries in computed tomography images based on the framework of active contours. In the proposed method, both global and local intensity information is utilized in the energy calculation. The global term is defined as a normalized cumulative distribution function, which contributes to the overall active contour energy in an adaptive fashion based on image histograms, to deform the active contour away from local stationary points. Possible outliers, such as kissing vessel artifacts, are removed in the postprocessing stage by a slice-by-slice correction scheme based on multiregion competition, where both arteries and kissing vessels are identified and tracked through the slices. The efficiency and the accuracy of the proposed technique are demonstrated on both synthetic and real datasets. The results on clinical datasets show that the method is able to extract the major branches of arteries with an average distance of 0.73 voxels to the manually delineated ground truth data. In the presence of kissing vessel artifacts, the outer surface of the entire coronary tree, extracted by the proposed algorithm, is smooth and contains fewer erroneous regions, originating in kissing vessel artifacts, as compared to the initial segmentation.

In the forward EIT-problem numerical solutions of an elliptic partial differential equation are required. Given the arbitrary geometries encountered, the Finite Element Method (FEM) is, naturally, the method of choice. Nowadays, in EIT applications, there is an increasing demand for finer Finite Element mesh models. This in turn results to a soaring number of degrees of freedom and an excessive number of unknowns. As such, only piece-wise linear basis functions can practically be employed to maintain inexpensive computations. In addition, domain reduction and/or compression schemes are often sought to further counteract for the growing number of unknowns. In this paper, we replace the piece-wise linear with wavelet basis functions (coupled with the domain embedding method) to enable sparse approximations of the forward computations. Given that the forward solutions are repeatedly, if not extensively, utilised during the image reconstruction process, considerable computational savings can be recorded whilst maintaining O(N) forward problem complexity. We verify with numerical results that, in practice, less than 5% of the involved coefficients are actually required for computations and, hence, needs to be stored. We finalise this work by addressing the impact to the inverse problem. It is worth underlining that the proposed scheme is independent of the actual family of wavelet basis functions of compact support.

In this paper, we employ the concept of the Fisher information matrix (FIM) to reformulate and improve on the "Newton's One-Step Error Reconstructor" (NOSER) algorithm. FIM is a systematic approach for incorporating statistical properties of noise, modeling errors and multi-frequency data. The method is discussed in a maximum likelihood estimator (MLE) setting. The ill-posedness of the inverse problem is mitigated by means of a nonlinear regularization strategy. It is shown that the overall approach reduces to the maximum a posteriori estimator (MAP) with the prior (conductivity vector) described by a multivariate normal distribution. The covariance matrix of the prior is a diagonal matrix and is computed directly from the Fisher information matrix. An eigenvalue analysis is presented, revealing the advantages of using this prior to a Gaussian smoothness prior (Laplace). Reconstructions are shown using measured data obtained from a shallow breathing of an adult human subject. The reconstructions show that the FIM approach clearly improves on the original NOSER algorithm.

We investigate on the use of the Domain Embedding Method (DEM) for the forward modelling in EIT. This approach is suitably configured to overcome the model meshing bottleneck since it does not require that the mesh on the domain is adapted to the boundary surface. This is of crucial importance for, e.g., clinical applications of EIT, as it avoids tedious and time-consuming (re-)meshing procedures. The suggested DEM approach can accommodate arbitrary yet Lipschitz smooth boundary surfaces and is not limited to polygonal domains. For the discretisation purposes, we employ B-splines as they allow for arbitrary accuracy by raising the polynomial degree and are easy to implement due to their inherent piecewise polynomial structure. Numerical experiments confirm that a B-spline discretization yields, similarly to conventional Finite Difference discretizations, increasing condition numbers of the system matrix with respect to the discretisation levels. Fortunately, multiresolution ideas based on B-splines allow for optimal wavelet preconditioning.

Wearable human movement measurement systems are increasingly popular as a means of capturing human movement data in real-world situations. Previous work has attempted to estimate segment kinematics during walking from foot acceleration and angular velocity data. In this paper, we propose a novel neural network [GRNN with Auxiliary Similarity Information (GASI)] that estimates joint kinematics by taking account of proximity and gait trajectory slope information through adaptive weighting. Furthermore, multiple kernel bandwidth parameters are used that can adapt to the local data density. To demonstrate the value of the GASI algorithm, hip, knee, and ankle joint motions are estimated from acceleration and angular velocity data for the foot and shank, collected using commercially available wearable sensors. Reference hip, knee, and ankle kinematic data were obtained using externally mounted reflective markers and infrared cameras for subjects while they walked at different speeds. The results provide further evidence that a neural net approach to the estimation of joint kinematics is feasible and shows promise, but other practical issues must be addressed before this approach is mature enough for clinical implementation. Furthermore, they demonstrate the utility of the new GASI algorithm for making estimates from continuous periodic data that include noise and a significant level of variability.

Objective: To develop and implement a method for three-dimensional (3D) reconstruction of coronary arteries from conventional monoplane angiograms., Background: 3D reconstruction of conventional coronary angiograms is a promising imaging modality for both diagnostic and interventional purposes., Methods: Our method combines image enhancement, automatic edge detection, an iterative method to reconstruct the centerline of the artery and reconstruction of the diameter of the vessel by taking into consideration foreshortening effects. The X-Ray-based 3D coronary trees were compared against phantom data from a virtual arterial tree projected into two planes as well as computed tomography (CT)-based coronary artery reconstructions in patients subjected to coronary angiography., Results: Comparison against the phantom arterial tree demonstrated perfect agreement with the developed algorithm. Visual comparison against the CT-based reconstruction was performed in the 3D space, in terms of the direction angle along the centerline length of the left anterior descending and circumflex arteries relative to the main stem, and location and take-off angle of sample bifurcation branches from the main coronary arteries. Only minimal differences were detected between the two methods. Inter- and intraobserver variability of our method was low (intra-class correlation coefficients > 0.8)., Conclusion: The developed method for coronary artery reconstruction from conventional angiography images provides the geometry of coronary arteries in the 3D space., (Copyright 2008 Wiley-Liss, Inc.)

This paper proposes a novel algorithm for function approximation that extends the standard generalized regression neural network. Instead of a single bandwidth for all the kernels, we employ a multiple-bandwidth configuration. However, unlike previous works that use clustering of the training data for the reduction of the number of bandwidths, we propose a distinct scheme that manages a dramatic bandwidth reduction while preserving the required model complexity. In this scheme, the algorithm partitions the training patterns to groups, where all patterns within each group share the same bandwidth. Grouping relies on the analysis of the local nearest neighbor distance information around the patterns and the principal component analysis with fuzzy clustering. Furthermore, we use a hybrid optimization procedure combining a very efficient variant of the particle swarm optimizer and a quasi-Newton method for global optimization and locally optimal fine-tuning of the network bandwidths. Training is based on the minimization of a flexible adaptation of the leave-one-out validation error that enhances the network generalization. We test the proposed algorithm with real and synthetic datasets, and results show that it exhibits competitive regression performance compared to other techniques.

This paper proposes a new nonparametric regression method, based on the combination of generalized regression neural networks (GRNNs), density-dependent multiple kernel bandwidths, and regularization. The presented model is generic and substitutes the very large number of bandwidths with a much smaller number of trainable weights that control the regression model. It depends on sets of extracted data density features which reflect the density properties and distribution irregularities of the training data sets. We provide an efficient initialization scheme and a second-order algorithm to train the model, as well as an overfitting control mechanism based on Bayesian regularization. Numerical results show that the proposed network manages to reduce significantly the computational demands of having individual bandwidths, while at the same time, provides competitive function approximation accuracy in relation to existing methods.

Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6).