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Restoring gastrointestinal continuity has remained an issue since the first successful gastrectomy. Although various reconstruction techniques are possible after gastrectomy, researchers and clinicians have yet to decide on an optimal or ideal reconstruction method. When choosing a reconstruction method, surgeons should seek to maintain, in addition to surgical and oncological stability, quality of life by ensuring nutritional intake after surgery.

Robotic technology has been introduced to gastric cancer surgery more recently than laparoscopic methods. Therefore, most robotic procedures, including radical lymphadenectomy and reconstructions, are based on those of laparoscopic surgery. Currently, various reconstruction techniques are used in laparoscopic gastrectomy, and these are currently reproduced by the robotic surgical systems, utilizing the improved dexterity resulting from the internal articulated endoscopic wrist [1–6].

Background We evaluated the diagnostic implications of a small-voxel reconstruction for lymph node characterization in breast cancer patients, using state-of-the-art FDG-PET/CT. We included 69 FDG-PET/CT scans from breast cancer patients. PET data were reconstructed using standard 4 × 4 × 4 mm3 and small 2 × 2 × 2 mm3 voxels. Two hundred thirty loco-regional lymph nodes were included, of which 209 nodes were visualised on PET/CT. All nodes were visually scored as benign or malignant, and SUVmax and TBratio(=SUVmax/SUVbackground) were measured. Final diagnosis was based on histological or imaging information. We determined the accuracy, sensitivity and specificity for both reconstruction methods and calculated optimal cut-off values to distinguish benign from malignant nodes. Results Sixty-one benign and 169 malignant lymph nodes were included. Visual evaluation accuracy was 73% (sensitivity 67%, specificity 89%) on standard-voxel images and 77% (sensitivity 78%, specificity 74%) on small-voxel images (p = 0.13). Across malignant nodes visualised on PET/CT, the small-voxel score was more often correct compared with the standard-voxel score (89 vs. 76%, p

Photogrammetry is one of the most used techniques for monitoring and surveying. It is widely used in several applications and in different working conditions. Accuracy of photogrammetry reconstruction methods may change depending on the working conditions (e.g. the number of acquired images, lighting conditions, baselines between images), and it is strictly related to the success of the solution of the Structure from Motion problem. Despite its widely spread use and the ever growing improvements to the reconstruction technique, photogrammetry still does not reach the same level of reliability of laser scanning surveying techniques: significant issues may occur in photogrammetric reconstructions when in presence of lighting problems or when the object of interest is not sufficiently textured. However, it relies on the use of much cheaper tools with respect to laser scanning techniques and surveying is usually much faster. This paper aims at showing the potential improvement that can be obtained by introducing information provided by the navigation system in the 3D reconstruction algorithm: the goal is that of making the solution algorithm of the Structure from Motion problem more reliable and accurate. As a side effect, faster reconstruction is typically achieved. The technique is validated on a building using images and navigation information got from a standard smartphone.

Articular surface lesions can be limited to cartilage or can extend to the underlying subchondral bone as it is observed in osteochondritis dissecans (OCD), osteonecrosis and osteochondral fractures. Deep ostechondral defects should be treated with surgical techniques which reconstruct either bone and chondral layers of the defect. Authors propose to divide currently used methods into 4 groups: osteochondral transfers, biologic, hybrid and synthetic reconstruction methods. The type of reconstruction method implies specific postoperative treatment and so more important, a rehabilitation protocol should be individually modified. In our opinion, the best way for proper controlling of the osteochondral graft maturation is periodically checkout of the graft status using the monitoring MR protocol after 3 or 6 weeks subsequently 6 and 12 months postoperatively. Depending on the the bone layer status and subchondral lamina quality patients might be allowed for more physical activity or restricted within. In cases of the slow maturation process, physicians may modified pharmacotherapy or/and physiotherapy.

There are still many controversial issues on the technical aspect of pancreatoduodenectomy (PD). Surgical decision should be made on the basis of principles of oncologic surgery and evidences – evidence-based surgery. Clinically randomized controlled trial (RCT) is considered providing the highest level of evidence. However, even similarly designed RCTs sometimes show different results, so that many systematic review and meta-analysis studies are performed. Pancreatic surgeons cannot simply choose one of the two types of surgery following the result of RCT, and they tend to customize PD according to tumor factor and patient factors. Tumor factors include nature, origin, location, and extent of tumor. According to the tumor origin, the area that should be focused during PD could be different. Extent of resection sometimes should be customized according to the location of the tumor. According to the extent of tumor, sometimes resections of other organs or major vessels are needed. Different reconstruction techniques can be applied according to the parenchymal condition and ductal diameter of the pancreas. Host factors including old age and operative risk are the factors that might affect surgical decision also. In this chapter, potential procedures that can be customized are introduced. Some have evidences and others need evidence. In conclusion, PD should be customized according to the disease and host factors. Pancreatic surgeons should be familiar with every type of resection and reconstruction method so as to be able to customize PD for each patient.

Tilted-wave interferometry (TWI) is a novel measurement technique for the highly accurate optical measurement of aspheres and freeform surfaces [1-7]. It combines interferometric measurements with ray tracing and mathematical reconstruction procedures. The first results of a sensitivity analysis were given in [8]. The results support the feasibility of the basic TWI principle, and good reconstruction results were obtained for surfaces whose deviations from the design topography are described by Zernike polynomials. The TWI reconstruction procedure is divided into two parts. The first part reconstructs the long-wave deviation of the specimen from its design form. The second part aims at the reconstruction of high spatial frequencies [7]. This paper investigates the robustness of the long-wave TWI reconstruction method. Therefore, we extend the sensitivity analysis to surfaces whose deviation from their design topography no longer corresponds to a Zernike polynomial. We utilize a simulation environment that was developed at PTB for assessing optical measuring systems [8, 9]. Reconstruction accuracies are then investigated in dependence on the variation of the specimen’s topography and the measurement noise. Different variations of the specimen’s topography with different spatial frequencies are studied. Furthermore, the influence of the deviation of the vertex radius of the design topography is investigated. Throughout all simulations no errors in the characterization of the TWI were assumed to be present which corresponds to a perfect calibration.

OSPF (Open Shortest Path First), which is used widely on networks, has a Router Dead Interval problem. If a (backup) designated router has stopped operation due to failure, the OSPF routers await a hello packet acknowledgment for the router dead interval to recognize that the designated router has ceased the operation. The Router Dead Interval is 40 sec This interval time is not only long for many real-time applications but also involves huge buffering of data and a burst of traffic after the router reconstruction. To avoid the Router Dead Interval, we propose a fast method of designated router detection by enhanced OSPF. In this report, we show how our method reduces the route reconstruction time from 45 sec to 10 or less on OSPF networks.

Obtaining 3-D reconstruction directly and expediently for the real world has became a hot topic in many fields. A 3-D reconstruction method of three views based on manifold study is proposed. Firstly, the fundamental matrix is estimated by adjacent view and optimized under three views constraint. Then 3-D point cloud is reconstructed after getting the projection matrixes of views. Further more, benefitting from minimum spanning tree, outliers are almost excluded. To increase point cloud’s density, the optimized 3-D point cloud is interpolated based on Radial Basis Function. Afterwards, the dense point cloud is mapped to two dimensional plane using manifold study algorithm, and then divided into plane Delaunay triangle nets. Completing that, the topological relations of points are mapped back to 3-D space and 3-D reconstruction is realized. Many experiments show the method proposed in paper can achieve 3-D reconstruction for three views with quite good results.

The knowledge of the three-dimensional structure of proteins is crucial for understanding many important biological processes. Most biologically important proteins are too large to handle for the classical simulation tools. In such cases, coarse-grained (CG) models nowadays offer various opportunities for efficient conformational sampling and thus prediction of the three-dimensional structure. A variety of CG models have been proposed, each based on a similar framework consisting of a set of conceptual components such as protein representation, force field, sampling, etc. In this chapter we discuss these components, highlighting ideas which have proven to be the most successful. As CG methods are usually part of multistage procedures, we also describe approaches used for the incorporation of homology data and all-atom reconstruction methods.

A Takagi-Sugeno sliding mode observer (TS SMO) is used to detect and reconstruct parameter faults in the actuator dynamics of wind turbines. The actuator dynamics of the pitch system and of the generator are modeled as first or second order delay models. By choosing appropriate actuator fault matrices for the TS SMO design, parameter changes or faults in the actuator dynamics can be calculated from the reconstructed additive fault signal. Example simulations are performed using both a reduced-order model and the aero-elastic wind turbine simulation FAST by NREL. The presented fault reconstruction method appears promising to be used for fast fault detection schemes. The accuracy of the reconstruction, however, is still in need of improvement and the method lacks robustness with respect to model uncertainties, since no satisfactory reconstruction can be achieved in FAST simulations.

Cophylogenetic analysis seeks to explain the relationships between mutually evolving pairs of species such as hosts and parasites. In the last two decades, increasingly sophisticated computational methods have been developed for performing cophylogenetic analyses. In particular, event-based reconstruction methods attempt to find the best supported reconstructions of pairs of related trees using a set of events including cospeciation, duplication, transfer, and loss. This chapter formulates the cophylogeny reconstruction problem, describes the algorithmic techniques that have been developed for this problem, and compares and contrasts the software packages that implement these methods.

One approach to investigating neural death is through systematic studies of the changing morphology of cultured brain neurons in response to cellular challenges. Image segmentation and neuron skeleton reconstruction methods developed to date to analyze such changes have been limited by the low contrast of cells. In this paper we present new algorithms that successfully circumvent these problems. The binary method is based on logical analysis of grey and distance difference of images. The spurious regions are detected and removed through use of a hierarchical window filter. The skeletons of binary cell images are extracted. The extension direction and connection points of broken cell skeletons are automatically determined, and broken neural skeletons are reconstructed. The spurious strokes are deleted based on cell prior knowledge. The reconstructed skeletons are processed furthermore by filling holes, smoothing and extracting new skeletons. The final constructed neuron skeletons are analyzed and calculated to find the length and morphology of skeleton branches automatically. The efficacy of the developed algorithms is demonstrated here through a test of cultured brain neurons from newborn mice.

While multi-view stereo reconstruction of Lambertian surfaces is nowadays highly robust, reconstruction methods based on correspondence search usually fail in the presence of ambiguous information, like in the case of partially reflecting and transparent surfaces. On the epipolar plane images of a 4D light field, however, surfaces like these give rise to overlaid patterns of oriented lines. We show that these can be identified and analyzed quickly and accurately with higher order structure tensors. The resulting method can reconstruct with high precision both the geometry of the surface as well as the geometry of the reflected or transmitted object. Accuracy and feasibility are shown on both ray-traced synthetic scenes and real-world data recorded by our gantry.

Record linking is the task of detecting records in several databases that refer to the same entity. This task aims at exploring the relationship between entities, which normally lack common identifiers in heterogeneous datasets. When entities contain multiple relational records, linking them across datasets can be more accurate by treating the records as groups, which leads to group linking methods. Even so, individual record links may still be needed for the final group linking step. This problem can be solved by multiple instance learning, in which group links are modelled as bags, and record links are considered as instances. In this paper, we propose a novel method for instance classification and group record linkage via bag reconstruction from instances. The bag reconstruction is based on the modeling of the distribution of negative instances in the training bags via kernel density estimation. We evaluate this approach on both synthetic and real-world data. Our results show that the proposed method can outperform several baseline methods.

In this paper binary tomography on the triangular grid is addressed. We use three and six natural projections according to the structure of the grid. We propose an energy-minimization method based on the simulated annealing algorithm to reconstruct the original images. Our method is shown in four regular hexagon shaped test images of approximately 4000 pixels.

P2P technology can avoid "single point of failure" and "hot spots bottleneck" problems, which exist in traditional centralized system of spatial information. Hybrid P2P Spatial Indexing Network combines distributed Quad-Tree with DHT-based Chord network to maintain both query efficiency and system load balance. In this paper, key technology of HPSIN based distributed vector geo-data online service is studied, the pattern of vector geo-data organization based on Linking Mechanism, segmentation and lossless reconstruction are proposed. This novel organization pattern can be used to form a loosely and globally distributed topology. Segmentation and lossless reconstruction method takes advantage of linking information, to reconstruct the damaged Geometries lossless. Comparative experiment shows that the organization and the associated algorithms are lossless and reliable.

The aim of this chapter is to review direct (non-iterative) anomaly detection algorithms that take advantage of the smallness of the ultrasound anomalies. In particular, we numerically investigate their stability with respect to medium and measurement noises as well as their resolution.

Motion capture experiments are often used in coordination with digital human modeling to offer insight into the simulation of real-world tasks or as a means of validating existing simulations. However, there is a gap between the motion capture experiments and the simulation models, because the motion capture system is based on Cartesian space while the simulation models are based on joint space. This paper bridges the gap and presents a methodology that enables one to map joint angles of motion capture experiments to simulation models in order to obtain the same posture. The posture reconstruction method is an optimization-based approach where the cost function is a constant and constraints include (1) the distances between simulation model joint centers and the corresponding experimental subject joint centers are equal to zeros; (2) all joint angles are within joint limits. Examples are used to demonstrate the effectiveness of the proposed method.

In this paper we present a deterministic binary tomography reconstruction method based on the Spectral Projected Gradient (SPG) optimization approach. We consider a reconstruction problem with added smoothness convex prior. Using a convex-concave regularization we reformulate this problem to a non-integer and box constrained optimization problem which is suitable to solve by SPG method. The flexibility of the proposed method allows application of other reconstruction priors too. Performance of the proposed method is evaluated by experiments on the limited set of artificial data and also by comparing the obtained results with the ones provided by the often used non-deterministic Simulated Annealing method. The comparison shows its competence regarding to the quality of reconstructions.

With the raising of the third session of the CPPCC national committee first proposal” promote the development of china’s law-carbon economy on the pro- postal”, the R&D and result. This article studies deeply and reconstructs cosmically the structure of the furnace, aiming at the energy saving method of the furnace’s energy saving application, in order to achieve the saving of fuel as could be under the temperature of material outputting and ensuring the safety. What has been discussed above still need more proofs during the practice, achieving the best result through various kinds of technic improvement and cooperation.

International audience; We propose a method for multi-view reconstruction from videos adapted to dynamic cluttered scenes under uncontrolled imaging conditions. Taking visibility into account, and being based on a global optimization of a true spatio-temporal energy, it oilers several desirable properties: no need for silhouettes, robustness to noise, independent from any initialization, no heuristic force, reduced flickering results, etc. Results on real-world data proves the potential of what is, to our knowledge, the only globally optimal spatio-temporal multi-view reconstruction method.

The 3D face reconstruction method of different race is proposed in this paper. It chose different standard face model according to different race, and adjusted and combined with the extracted character and corresponding model, thereby acquired the personalized model that reflected different race, then created realistic 3D face adding grain information by the texture mapping technique. The final result and quantitative analysis showed that: the character could adapt to standard face model more effectively and reconstruct realistic 3D face successfully.

Dual-Energy Contrast Enhanced Digital breast tomosynthesis is an emerging technique for breast cancer detection, which combines the strengths of functional and morphological 3D imaging The projection images acquired with two energy spectra at several angulations are combined to obtain “iodine” projections These are then reconstructed to provide 3D iodine images The combination process significantly increases the noise in the images, which is further amplified by the 3D reconstruction This paper proposes a regularized reconstruction method based on the simultaneous algebraic reconstruction technique to be used for the reconstruction of the iodine volume The regularization represents a constraint for the reconstructed volume, which causes the reduction of the noise and preserves the structures of interest Preliminary results on clinical data demonstrate a significant increase of the visibility of iodine-enhanced regions without affecting their sharpness and morphology.

In the last two decades, extensive literature [6-10] has been produced on the research of super-resolution image reconstruction. The research has witnessed the introduction of many techniques in order to achieve high-resolution images from a sequence of geometrically warped, blurred, noisy, and under-sampled low-resolution images. Each of these techniques is either an extension of some previous methodologies or differs from the other techniques in their assumptions of the observation model or the type of reconstruction method applied for achieving the high-resolution images. On a broader aspect, we divide the various super-resolution techniques depending upon the domain (spatial or frequency) they are based on.

In the concern of speeding the acquisition time parallel magnetic resonance imaging (pMRI), that uses data obtained simultaneously from several receiver coils with distinct spatial sensitivities, is often used. Several solutions for combining data from involved coils have already been presented. The goal of this paper is to propose a new solution which takes advantage of the benefits of the analytic image concept for reconstructing pMRI. The proposed method was evaluated by experiments on mouse brain data and compared with the conventional Sum-of-Square and Roemer reconstruction techniques. According to the satisfactory results, both in qualitative and quantitative meaning, provided by the proposed reconstruction method, reconstructed images can be used as reference in many parallel acquisition schemas.

In this paper, we present ultrasonic array reconstruction techniques aiming at localizing and characterizing flaws in an inspected piece. The proposed algorithms derive from the synthetic focusing approach, which exploits the time-of-flight information. Theoretical forward models implemented in the CIVA software platform are able to provide this information. An advantage is that these models can deal with complex inspection configurations, in particular with complex geometries (irregular surface, for example). The algorithms can be applied to data issued from various operating modes of array inspection. We describe the different algorithms and show examples of their applications for both simulated and measured data.

Reconstructing GRN from microarray dataset is a very challenging problem as these datasets typically have large number of genes and less number of samples. Moreover, the reconstruction task becomes further complicated as there are no suitable synthetic datasets available for validation and evaluation of GRN reconstruction techniques. Synthetic datasets allow validating new techniques and approaches since the underlying mechanisms of the GRNs, generated from these datasets, are completely known. In this paper, we present an approach for synthetically generating gene networks using causal relationships. The synthetic networks can have varying topologies such as small world, random, scale free, or hierarchical topologies based on the well-defined GRN properties. These artificial but realistic GRN networks provide a simulation environment similar to a real-life laboratory microarray experiment. These networks also provide a mechanism for studying the robustness of reconstruction methods to individual and combination of parametric changes such as topology, noise (background and experimental noise) and time delays. Studies involving complicated interactions such as feedback loops, oscillations, bi-stability, dynamic behavior, vertex in-degree changes and number of samples can also be carried out by the proposed synthetic GRN networks.

Scientific image processing involves a variety of problems including image modeling, reconstruction, and synthesis. In this paper we develop a constrained sampling approach for porous media synthesis and reconstruction in order to generate artificial samples of porous media. Our approach is different from current porous media reconstruction methods in which the Gibbs probability distribution is maximized by simulated annealing. We show that the artificial images generated by those methods do not contain the variability that typical samples of random fields are required to have.

Filtered backprojection (FBP) is an inadequate method to cope with inconsistencies in Radon space and, consequently, leads to artifacts in reconstructed CT images. A solution to this problem is given by statistical reconstruction methods like the Maximum-Likelihood Expectation-Maximization (MLEM) algorithm. The advantage of MLEM is that it allows to weight raw projection data during reconstruction. The method presented here consists of two steps. In a first step, inconsistent data in the Radon space were bridged using a directional interpolation scheme. Since these surrogate data are contaminated with residual inconsistencies, in a second step, the image is reconstructed using a weighted MLEM algorithm. In this work, the modified MLEM algorithm for metal artifact reduction in CT is presented for clinical hip prosthesis data. On the basis of image entropy the reconstruction success is evaluated.

Dynamic reconstruction methods are studied for the small animal PET (positron emission tomography) scanner ClearPET™ Neuro. In dynamic reconstruction the data are usually sorted into timeframes and reconstructed independently of each other. Using this timeframe approach, an appropriate trade-off between time resolution and noise has to be found. A more advanced method is dynamic reconstruction with temporal basis functions, where voxel values are time dependent modeled as weighted sum of basis functions. In a simulated example list-mode data are generated for the ClearPET™ Neuro and reconstructed with timeframe reconstruction and with dynamic reconstruction using B-Splines as temporal basis functions. Time activity curves are computed for various reconstructions with different timeframes and B-Splines. The example demonstrates the potential of dynamic reconstruction with temporal B-Splines.

The ability to measure the transcriptional response after a stimulus has drawn much attention to the underlying gene regulatory networks. Here, we evaluate the application of methods to reconstruct gene regulatory networks by applying them to the SOS response of E. coli, the budding yeast cell cycle and in silico models. For each network we define an a priori validation network, where each interaction is justified by at least one publication. In addition to the existing methods, we propose a SVD based method (NSS). Overall, most reconstruction methods perform well on in silico data sets, both in terms of topological reconstruction and predictability. For biological data sets the application of reconstruction methods is suitable to predict the expression of genes, whereas the topological reconstruction is only satisfactory with steadystate measurements. Surprisingly, the performance measured on in silico data does not correspond with the performance measured on biological data.

A novel reconstruction algorithm of order parameters based on Immunity Clonal Strategy (ICS) is presented in this paper, which combines the self-learning ability of Synergetic Neural Network (SNN) with the global searching performance of ICS to construct linear transform and then realize reconstruction. Compared with the reconstruction method based on Genetic Algorithm (GA), the new method not only overcomes the aimless and random searching of GA at the later time of searching but also improves its searching efficiency greatly. The tests on IRIS data and Brodatz texture show that the proposed method can positively find a new set of reconstruction parameters and enhance the classification accuracy rate remarkably.

The goal of sketch reconstruction is to take an inaccurate, 2D edge-vertex graph (i.e., sketch drawing) as input and reconstruct a 3D shape as output. However, traditional reconstruction methods based on image regularities tend to produce a distorted 3D shape. In part, this distortion is due to the inherent inaccuracies in the sketch, but it also relates to the failure to accurately distinguish between important and less important regularities. We propose a new self-correctional reconstruction algorithm that can progressively produce refined versions of sketch reconstructions. The algorithm corrects the shape and the drawing simultaneously using geometric error metrics. The proposed algorithm can minimize the distortion of the shape by adding 3D regularities to the image regularities. The self-correctional algorithm for minimizing the distortion of sketch reconstruction is discussed, and the experimental results show that the proposed method efficiently reconstructs more accurate 3D objects than previous ones.

Three-dimensional (3-D) models of outdoor scenes can be widely used in a number of fields such as object recognition, navigation, scenic simulation, and mixed reality. Such models are often made manually with high costs, so that automatic 3-D reconstruction has been widely investigated. In related works, a dense 3-D model is generated by using a stereo method. However, such approaches cannot use several hundred images together for dense depth estimation of large constructs and urban environments because it is difficult to accurately calibrate a large number of cameras. This paper proposes a novel dense 3-D reconstruction method that uses multiple image sequences. First, our method estimates extrinsic camera parameters of each image sequence, and then reconstructs a dense 3-D model of a scene using an extended multi-baseline stereo and voxel voting techniques., SCIA 2003 : 13th Scandinavian Conference on Image Analysis , Jun 29-Jul 2, 2003 , Halmstad, Sweden

Freehand 3D ultrasound imaging produces a set of irregularly spaced B-scans, which are typically reconstructed on a regular grid for visualisation and data analysis. Most standard reconstruction algorithms are designed to minimise computational requirements and do not exploit the underlying shape of the data.We investigate whether approximation with splines holds any promise as a better reconstruction method. A radial basis function approximation method is implemented and compared with three standard methods. While the radial basis approach is computationally expensive, it produces accurate reconstructions without the kind of visible artifacts common with the standard methods.