Geometrically accurate topology-correction of cortical surfaces using nonseparating loops

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.     

Automated graph-based analysis and correction of cortical volumetopology

The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. We present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. We apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. We tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects.     

Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.     

Topological median filters

This paper describes the definition and testing of a new type of median filter for images. The topological median filter implements some existing ideas and some new ideas on fuzzy connectedness to improve, over a conventional median filter, the extraction of edges in noise. The concept of alpha-connectivity is defined and used to create an algorithm for computing the degree of connectedness of a pixel to all the other pixels in an arbitrary neighborhood. The resulting connectivity map of the neighborhood effectively disconnects peaks in the neighborhood that are separated from the center pixel by a valley in the brightness topology. The median of the connectivity map is an estimate of the median of the peak or plateau to which the center pixel belongs. Unlike the conventional median filter, the topological median is relatively unaffected by disconnected features in the neighborhood of the center pixel. Four topological median filters are defined. Qualitative and statistical analyses of the four filters are presented. It is demonstrated that edge detection can be more accurate on topologically median filtered images than on conventionally median filtered images.     

Stereopsis-guided brain shift compensation

Brain deformation models have proven to be a powerful tool in compensating for soft tissue deformation during image-guided neurosurgery. The accuracy of these models can be improved by incorporating intraoperative measurements of brain motion. We have designed and implemented a passive intraoperative stereo vision system capable of estimating the three-dimensional shape of the surgical scene in near real-time. This intraoperative shape is compared with the cortical surface in the co-registered preoperative magnetic resonance (MR) volume for the estimation of the cortical motion resulting from the open cranial surgery. The estimated cortical motion is then used to guide a full brain model, which updates a preoperative MR volume. We have found that the stereo vision system is accurate to within approximately 1 mm. Based on data from two representative clinical cases, we show that stereopsis guidance improves the accuracy of brain shift compensation both at and below the cortical surface.     

Brain surface conformal parameterization using Riemann surface structure

In medical imaging, parameterized 3-D surface models are useful for anatomical modeling and visualization, statistical comparisons of anatomy, and surface-based registration and signal processing. Here we introduce a parameterization method based on Riemann surface structure, which uses a special curvilinear net structure (conformal net) to partition the surface into a set of patches that can each be conformally mapped to a parallelogram. The resulting surface subdivision and the parameterizations of the components are intrinsic and stable (their solutions tend to be smooth functions and the boundary conditions of the Dirichlet problem can be enforced). Conformal parameterization also helps transform partial differential equations (PDEs) that may be defined on 3-D brain surface manifolds to modified PDEs on a two-dimensional parameter domain. Since the Jacobian matrix of a conformal parameterization is diagonal, the modified PDE on the parameter domain is readily solved. To illustrate our techniques, we computed parameterizations for several types of anatomical surfaces in 3-D magnetic resonance imaging scans of the brain, including the cerebral cortex, hippocampi, and lateral ventricles. For surfaces that are topologically homeomorphic to each other and have similar geometrical structures, we show that the parameterization results are consistent and the subdivided surfaces can be matched to each other. Finally, we present an automatic sulcal landmark location algorithm by solving PDEs on cortical surfaces. The landmark detection results are used as constraints for building conformal maps between surfaces that also match explicitly defined landmarks.     

Interpolated Coarse Models for Microwave Design Optimization With Space Mapping

The efficiency of space-mapping optimization depends on the quality of the underlying coarse model, which should be sufficiently close to the fine model and cheap to evaluate. In practice, available coarse models are often cheap, but inaccurate (e.g., a circuit equivalent of the microwave structure) or accurate, but too expensive (e.g., a coarse-mesh model). In either case, the space-mapping optimization process exhibits substantial computational overhead due to the excessive fine model evaluations necessary to find a good solution if the coarse model is inaccurate, or due to the cost of the parameter extraction and surrogate optimization sub-problems if the coarse model is too expensive. In this paper, we use an interpolation technique, which allows us to create coarse models that are both accurate and cheap. This overcomes the accuracy/cost dilemma described above, permitting significant reduction of the space-mapping optimization time. Examples verify the performance of our approach.     

Self-repelling snakes for topology-preserving segmentation models.

The implicit framework of the level-set method has several advantages when tracking propagating fronts. Indeed, the evolving contour is embedded in a higher dimensional level-set function and its evolution can be phrased in terms of a Eulerian formulation. The ability of this intrinsic method to handle topological changes (merging and breaking) makes it useful in a wide range of applications (fluid mechanics, computer vision) and particularly in image segmentation, the main subject of this paper. Nevertheless, in some applications, this topological flexibility turns out to be undesirable: for instance, when the shape to be detected has a known topology, or when the resulting shape must be homeomorphic to the initial one. The necessity of designing topology-preserving processes arises in medical imaging, for example, in the human cortex reconstruction. It is known that the human cortex has a spherical topology so throughout the reconstruction process this topological feature must be preserved. Therefore, we propose in this paper a segmentation model based on an implicit level-set formulation and on the geodesic active contours, in which a topological constraint is enforced.     

Design of survivable communications networks under performanceconstraints

The authors describe a network design algorithm that uses a set of deterministic connectivity measures which result in topologically survivable network designs that also meet processing and performance requirements. The authors briefly describe some applicable graph theoretic concepts and recently developed connectivity measures. They describe systematic procedures for improving the topological survivability of a network, and the overall network design process. A design example is presented     

Hamilton-Jacobi skeleton on cortical surfaces

In this paper, we propose a new method to construct graphical representations of cortical folding patterns by computing skeletons on triangulated cortical surfaces. In our approach, a cortical surface is first partitioned into sulcal and gyral regions via the solution of a variational problem using graph cuts, which can guarantee global optimality. After that, we extend the method of Hamilton-Jacobi skeleton to subsets of triangulated surfaces, together with a geometrically intuitive pruning process that can trade off between skeleton complexity and the completeness of representing folding patterns. Compared with previous work that uses skeletons of 3-D volumes to represent sulcal patterns, the skeletons on cortical surfaces can be easily decomposed into branches and provide a simpler way to construct graphical representations of cortical morphometry. In our experiments, we demonstrate our method on two different cortical surface models, its ability of capturing major sulcal patterns and its application to compute skeletons of gyral regions.     

Image guidance of breast cancer surgery using 3-D ultrasound imagesand augmented reality visualization

This paper describes augmented reality visualization for the guidance of breast-conservative cancer surgery using ultrasonic images acquired in the operating room just before surgical resection. By combining an optical three-dimensional (3-D) position sensor, the position and orientation of each ultrasonic cross section are precisely measured to reconstruct geometrically accurate 3-D tumor models from the acquired ultrasonic images. Similarly, the 3-D position and orientation of a video camera are obtained to integrate video and ultrasonic images in a geometrically accurate manner. Superimposing the 3-D tumor models onto live video images of the patient's breast enables the surgeon to perceive the exact 3-D position of the tumor, including irregular cancer invasions which cannot be perceived by touch, as if it were visible through the breast skin. Using the resultant visualization, the surgeon can determine the region for surgical resection in a more objective and accurate manner, thereby minimizing the risk of a relapse and maximizing breast conservation. The system was shown to be effective in experiments using phantom and clinical data     

Space Mapping With Multiple Coarse Models for Optimization of Microwave Components

The performance of space mapping (SM) optimization algorithms depends primarily on the quality of the underlying coarse model. Models available in the microwave area can be cheap but inaccurate or accurate but too expensive. Here, we consider a multicoarse-model technique that allows us to combine the merits of both types of coarse models to substantially reduce the overall computational cost of optimization in comparison to traditional SM.     

Registration under topological change for CT colonography

Computed tomography (CT) colonography is a minimally invasive screening technique for colorectal polyps, in which X-ray CT images of the distended colon are acquired, usually in the prone and supine positions of a single patient. Registration of segmented colon images from both positions will be useful for computer-assisted polyp detection. We have previously presented algorithms for registration of the prone and supine colons when both are well distended and there is a single connected lumen. However, due to inadequate bowel preparation or peristalsis, there may be collapsed segments in one or both of the colon images resulting in a topological change in the images. Such changes make deformable registration of the colon images difficult, and at present, there are no registration algorithms that can accommodate them. In this paper, we present an algorithm that can perform volume registration of prone/supine colon images in the presence of a topological change. For this purpose, 3-D volume images are embedded as a manifold in a 4-D space, and the manifold is evolved for nonrigid registration. Experiments using data from 24 patients show that the proposed method achieves good registration results in both the shape alignment of topologically different colon images from a single patient and the polyp location estimation between supine and prone colon images.     

Surface Band Tuning of Bi2Te3 Topological Insulator Thin Films by Gas Adsorption

The surface band tuning of the topological insulator Bi₂Te₃ by gas adsorption is investigated on the basis of ab?initio calculations. It is shown that, with the increase of Te vacancies, the topologically non-trivial surface state which originates from the second quintuple layer coexists with the topologically trivial surface. Molecular dynamics simulation reveals that O₂ and NO₂ easily occupy the Te vacancy sites and further bind to the Bi atoms from the second atomic layer. Moreover, the surface band with the Dirac cone is observed. Our results suggest that the topological surface state can be effectively regulated by NO₂ and O₂ adsorption.     

Exceptional Spin-to-Charge Conversion in Selective Band Topology of Bi/Bi1-xSbx with Spintronic Singularity

In this study, spin-to-charge conversion (SCC) of various topological materials with ferromagnet is investigated using spintronic terahertz (THz) emission spectroscopy. Compared with other topological materials, significantly large THz emission is observed for topologically nontrivial phases of Bi_1-xSb_x (x > 0.2) that predominantly originates from the topological surface state. When Bi is superposed above a certain stoichiometry of Bi_1-xSb_x, it plays a crucial role in generating a highly spin-split state and enhancing the spin-mixing conductance, resulting in colossal THz emission. This proves that improving the SCC efficiency through interface engineering is a useful strategy to design a powerful spintronic device. Collectively, this study proposes a methodology for systematically analyzing SCC efficiency or spin Hall angle using THz emission spectroscopy and offers an efficient structure for future spintronic devices.     

Moving Objects Detection Using Intersecting Cortical Model in Enhanced Fish-eye Image

A new method of the moving objects detection using the enhanced fish-eye lens and the intersecting cortical model (ICM) algorithm is proposed. The improved fish-eye lens is designed through controlling the entrance pupils of the lens.This lens has an ultra field of view about 183 degrees,and can image resolution.The ICM is a model based on pulse coupled neural network(PCNN) which is especially designed for image processing.It is derived from several visual cortex models and is basically the intersection of these models.The theoretical foundation of the ICM is given.An improved ICM algorithm in which some parameters are modified is used to detect moving objects specially.The experiment indicated that moving objects can be detected reliably and efficiently using ICM algorithm from the elliptical fish-eye image.It can be used in the field of traffic monitoring and other security domains.     

Empirical model generation techniques for planar microwave components using electromagnetic linear regression models

Accurate and efficient empirical model generation techniques of microwave devices, for a large range of geometric and material parameters opportunely chosen, are presented. The empirical models are based on multiple linear regression approach, which compensates the error between an initial inaccurate empirical model and an electromagnetic (EM) full-wave solver (or measurement data). The aim of these techniques is to generate accurate empirical models, which are computationally very efficient with respect to any EM technique. These simple models could be integrated in a toolbox of any commercially available computed-aided design tools for RF/microwave circuits. Comparisons with artificial neural networks and linear-regression-based models are listed and discussed for the dispersion of a microstrip transmission line propagating the quasi-TEM mode and a microwave tunable phase shifter propagating the even mode.     

Accurate Conjunction of Yield Models for Fault-Tolerant Memory Integrated Circuits

Critical defects, i.e., faults, inevitably occur during semiconductor fabrication, and they significantly reduce both manufacturing yield and product reliability. To decrease the effects of the defects, several fault-tolerance methods, such as the redundancy technique and the error correcting code (ECC), have been successfully applied to memory integrated circuits. In the semiconductor business, accurate estimation of yield and reliability is very important for determining the chip architecture as well as the production plan. However, a simple conjunction of previous fault-tolerant yield models tends to underestimate the manufacturing yield if several fault-tolerance techniques are employed simultaneously. This paper concentrates on developing and verifying an accurate yield model which can be applied successfully in such situations. The proposed conjunction model has been derived from the probability of remaining redundancies and the average number of defects after repairing the defects with the remaining redundancies. The validity of the conjunction yield model is verified by a Monte Carlo simulation.      

Large Damping Enhancement in Dirac-Semimetal–Ferromagnetic-Metal Layered Structures Caused by Topological Surface States

This article reports damping enhancement in a ferromagnetic NiFe thin film due to an adjacent α-Sn thin film. Ferromagnetic resonance studies show that an α-Sn film separated from a NiFe film by an ultrathin Ag spacer can cause an extra damping in the NiFe film that is three times bigger than the intrinsic damping of the NiFe film. Such an extra damping is absent in structures where the α-Sn film interfaces directly with a NiFe film, or is replaced by a β-Sn film. The data suggest that the extra damping is associated with topologically nontrivial surface states in the topological Dirac semimetal phase of the α-Sn film. This work suggests that, like topological insulators, topological Dirac semimetal α-Sn may have promising applications in spintronics.     

Cortical atrophy in Alzheimer's disease unmasks electrically silentsulci and lowers EEG dipolarity

Alzheimer's disease (AD) patients show lower dipolarity (goodness-of-fit) for dipole localizations of alpha or other dominant electroencephalography (EEG) frequency components in the occipital cortex. In the present study, we performed computer simulations to discover which of distributions of dipole activity lower dipolarity in a manner similar to that seen in severe AD. Dipolarity was estimated from simulations of various electric dipole generator configurations within the occipital cortex under conditions of widened cortical sulci (a severely demented AD case) or no sulcal widening (a normal subject). The cortical and scalp surfaces, derived from the subjects' MRI's, were assumed to be uniformly electrically conducting. Randomly placed, nonoverlapping lesions ranging from 1 to 4 mm2 per lesion were used in both the normal and AD models to simulate the electrical effect of neuropathological AD lesions. In both models, dipolarity decreased as total lesion size increased. However, the AD model showed lower dipolarity than the normal model for both individual lesion sizes and for larger total lesion sizes. The larger decline in dipolarity in the AD model appears to be due to sulcal widening which unmasks the effect of lesions buried within sulci. These simulations identify a possible mechanism explaining why sulcally-located neuropathological changes plus progressive cortical atrophy in AD brains (and presumably other cortical disorders producing atrophy) alter EEG patterns and dipolarity differently from normal cortex damaged by similar lesions.