Small Number Of Projections Research Articles

Limited view reconstruction for differential phase-contrast computed tomography

This paper describes an algebraic reconstruction algorithm that uses total variation (TV) regularization for differential phase contrast computed tomography (DPC-CT) using a limited number of views. In order to overcome over-flattening inherent in TV regularization, a two-step reconstruction process is used: we first reconstruct tomographic images of gradient refractive index from differential projections with TV regularization; these images are then used to compute tomographic images of refractive index by solving the Poisson equation. We incorporate TV regularization in the reconstruction process because the distribution of gradient refractive index is much more flattened than the refractive index. Simulations of the proposed method demonstrate that it can achieve satisfactory image quality from a much smaller number of projections than is required by the Nyquist sampling theorem. We experimentally prove the feasibility of the proposed method using dark field imaging optics at PF-14C beamline at the Photon Factory, KEK. The differential phase contrast projection data was experimentally acquired from a biological sample and DPC-CT images were reconstructed. We show that far fewer projections are needed when the proposed algorithm is used.

Evaluation of adaptive combination of 30-channel head receive coil array data in 23Na MR imaging.

The aim was to optimally combine multichannel coil array data in sodium ((23) Na) MRI. (23) Na MRI was conducted on a 3 Tesla MR system using a 30-channel head receive coil array. The parameters used for the adaptive combination (ADC) reconstruction of the low signal-to-noise ratio (SNR) dataset have been optimized by finding the maximum mean SNR. A pseudo multiple-replica approach has been used to obtain SNR maps of the combined images. To prove reproducibility of the combination algorithm, the procedure was repeated for several measurements. For low SNR data, sum-of-squares (SOS) reconstruction leads to high background noise and a signal bias in the imaged object. The ADC reconstruction clearly reduces noise in the image and leads to an increase of the mean SNR in the range of 8% to 50%, compared to weighted SOS depending on the absolute SNR of the image. The evaluation of the effects of different noise scans showed that a small number of projections can be used to estimate noise statistics of the coil array without substantially decreasing the resulting SNR. (23) Na MRI can be markedly improved by using a 30-channel receive array and ADC reconstruction. The ADC reconstruction showed robust results for all measurements without the need for sensitivity maps.

Binary image reconstruction from a small number of projections and the morphological skeleton

In binary tomography, the goal is to reconstruct binary images from a small set of their projections. This task can be underdetermined, meaning that several binary images can have the same projecti...

A multivariate statistical framework for the analysis of software effort phase distribution

ContextIn software project management, the distribution of resources to various project activities is one of the most challenging problems since it affects team productivity, product quality and project constraints related to budget and scheduling. ObjectiveThe study aims to (a) reveal the high complexity of modelling the effort usage proportion in different phases as well as the divergence from various rules-of-thumb in related literature, and (b) present a systematic data analysis framework, able to offer better interpretations and visualisation of the effort distributed in specific phases. MethodThe basis for the proposed multivariate statistical framework is Compositional Data Analysis, a methodology appropriate for proportions, along with other methods like the deviation from rules-of-thumb, the cluster analysis and the analysis of variance. The effort allocations to phases, as reported in around 1500 software projects of the ISBSG R11 repository, were transformed to vectors of proportions of the total effort and were analysed with respect to prime project attributes. ResultsThe proposed statistical framework was able to detect high dispersion among data, distribution inequality and various interesting correlations and trends, groupings and outliers, especially with respect to other categorical and continuous project attributes. Only a very small number of projects were found close to the rules-of-thumb from the related literature. Significant differences in the proportion of effort spent in different phrases for different types of projects were found. ConclusionThere is no simple model for the effort allocated to phases of software projects. The data from previous projects can provide valuable information regarding the distribution of the effort for various types of projects, through analysis with multivariate statistical methodologies. The proposed statistical framework is generic and can be easily applied in a similar sense to any dataset containing effort allocation to phases.

Stochastic diffusion equation with singular diffusivity and gradient-dependent noise in binary tomography

In this work, we use stochastic diffusion equation with a singular diffusivity and a gradient-dependent noise to improve the reconstruction of binary tomography cross-sections obtained from a small number of projections. A first reconstruction image is obtained with the Total Variation regularization method. The reconstruction is then refined with this stochastic approach. The method is applied to a noisy bone cross-section with 10 projection angles.

Bayesian reconstruction of projection reconstruction NMR (PR-NMR)

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work [1,2], it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700MHz HNCO spectrum of a protein HasA.

Reconstruction of heterogeneous materials via stochastic optimization of limited-angle X-ray tomographic projections

X-ray tomography has provided a non-destructive means for microstructure characterization in three and four dimensions. A stochastic procedure to accurately reconstruct material microstructure from limited-angle X-ray tomographic projections is presented and its utility is demonstrated by reconstructing a variety of distinct heterogeneous materials and elucidating the information content of different projection data sets. A small number of projections (e.g. 20–40) are necessary for accurate reconstructions via the stochastic procedure, indicating its high efficiency in using limited structural information.

Emotion regulation and conflict transformation in multi-team systems

Purpose– The aim of this paper is to test the moderating role of emotion regulation in the transformation of both task and process conflict into relationship conflict.Design/methodology/approach– A field study of multi-teams systems, in which (94) respondents are engaged in interpersonal and inter-team interactions, was conducted to test the effects of the interaction of emotion regulation and task and process conflict on the emergence of relationship conflict in 23 multi-team client/supplier systems.Findings– The findings show that when collective emotion regulation strategies are effective, process conflict is less likely to transform into relationship conflict. An emerging finding of this study shows that process conflict mediates the interaction between task conflict and emotion regulation on relationship conflict in multi-team systems.Research limitations/implications– This study uses a relatively small number of projects and participants: further studies with larger samples are recommended; in addition, longitudinal studies would allow for further testing the effect of team longevity in the emergence of effective emotion regulation strategies.ct transforming into relationship conflicts.Practical implications– The findings imply that managers of multi-team systems should actively try to stimulate their teams to develop effective emotion regulation strategies as effective emotion regulation mechanisms minimize the risk of process conflict transforming into relationship conflicts.Originality/value– The paper looks at a real-world (as opposed to lab-situation) environment; it addresses a contingency model of intra-group conflict and tests the transformation of task and process conflicts into relationship conflict taking into account the moderating effect of emotion regulation.

A Systems Perspective on Compressed Sensing and its Use in Reconstructing Sparse Networks

Compressed sensing (or compressive sensing), a relatively new area in signal processing, improves the efficiency of algorithms for compression, coding, and recovery of natural signals, including audio, still images, and video. Specifically, compressed sensing is concerned with the recovery of a sparse signal from a small number of projections onto random vectors. This paper discusses how systems thinking contributed to uncovering key concepts of compressed sensing and presents the development of an extension of compressed sensing to the sparse network reconstruction problem. The sparse network reconstruction problem is a reverse engineering problem of interest in a wide variety of fields, ranging from remote sensing to social networks.

Aligning Projection Images from Binary Volumes

In tomography, slight differences between the geometry of the scanner hardware and the geometric model used in the reconstruction lead to alignment artifacts. To exploit high-resolution detectors used in many applications of tomography, alignment of the projection data is essential. Markerless alignment algorithms are the preferred choice over alignment with markers, in case a fully automatic tomography pipeline is required. Moreover, marker based alignment is often not feasible or even possible. At the same time, markerless alignment methods often fail in scenarios where only a small number of projections are available. In this case, the angular separation between projection images is large and therefore the correlation between them is low. This is a property that most markerless algorithms rely on. The intermediate reconstruction problem of alignment by projection matching is highly underdetermined in the limited data case. Therefore, we propose a projection matching method that incorporates prior knowledge of the ground truth. We focus on reconstructing binary volumes. A discrete tomography algorithm is employed to generate intermediate reconstructions. This type of reconstruction algorithm does not rely heavily on correlated projection images. Our numerical results suggest that alignment using discrete tomography projection matching produces much better results in the limited angle case, when compared to a projection matching method that employs an algebraic reconstruction method.

The use of fine-wire EMG to investigate shoulder muscle recruitment patterns during cello bowing: The results of a pilot study

The physical mechanics of music making is important both in the prevention of injuries and in guiding how music is performed and taught. Electromyography has potential as a resource in understanding the loads involved in instrumental playing; however, only a small number of projects have been undertaken, and little is understood on the muscle activity used during bowing on string instruments. This study aimed to measure the muscle activity at the bowing shoulder of a cellist during cello playing and to establish if fine-wire EMG is useful in understanding muscle recruitment in string players without interfering with normal playing ability. This project used a combination of fine-wire and surface EMG to evaluate the muscular load placed on the right shoulder of a professional cellist whilst playing a set of various bowing exercises. The results indicated that different bowing techniques produced statistically different muscle activity levels, with the supraspinatus muscle in particular maintaining higher mean contraction (20% MVC) during all bowing patterns tested. Fine-wire EMG was useful in measuring shoulder muscle load and did not interfere with normal playing technique of the subject. Overall, the study presents a working protocol from which future studies may be able conduct further research.

SU-E-I-14: Improvement of Four Dimensional Cone Beam CT Image Quality with Iterative Reconstruction

Purpose: Four‐dimensional cone beam CT (4D‐CBCT) is well known as powerful modality for image guided radiation therapy. Conventionally, it is reconstructed by sorting the X‐ray projection images into each respiratory phase according to a breathing signal using the FDK algorithm. This usually leads to inadequate number of projections in each phase, resulting in low quality 4D‐CBCT images with obvious streaking artifacts. We tried to improve the image quality for 4D CBCT using iterative reconstruction method (ML‐convex) and compared with the images by FDK in SNR of the reconstructed images. Methods: We used ML‐convex algorithm, which has the ability to reconstruct CBCT images from a few number of noisy x‐ray projections by taking knowledge of x‐ray photon statistics into account. We compared the image quality of 4D CBCT by ML‐convex with that by FDK using projection images acquired with different rotational speeds by XVI 5.0 (Elekta). Projection images were sorted by 10 phases for both reconstruction algorithms. A phantom study and patient study were performed. The convergence rate and reconstruction accuracy were evaluated using Catphan‐600 physical phantom. Results: Image quality of 4D‐CBCT is substantially enhanced by the ML‐convex algorithm. Streaking artifacts, commonly presented in the image reconstructed by FDK algorithm, are almost suppressed in spite of the small number of projections. Quantitative evaluations indicated that, compared with the FDK results, ML‐convex method improves signal‐to‐noise‐ratio (SNR) and reconstructed more accurate CT value. Conclusion: The ML‐convex algorithm yielded images with higher SNR and more accurate CT value than those produced by FDK algorithm. This Result will enable us to apply 4D CBCT by ML‐convex algorithm to Image Guided Adaptive Radiation Therapy and patient daily CBCT‐based treatment localization in real clinical environments with the higher computational efficiency.

산촌생태마을 유형별 주민 보완 요구 및 기대 효과 분석

This study aimed to categorize mountain villages according to business expense investments and investigate residents' supplementation demands and differences between expected effects by the categories. For the study, 35 villages where are completely developed on and after 2007 were selected to consider fairness among local governments. Each village leader(the head of the village or the chairperson of the management committee) was conducted a survey from 2012 May to August and one copy of survey was eliminated from the study. The study is summarized into four results. First, the types of mountain villages were categorized as a type of mountain village practice(18), a type of life environment improvement(8) and a type of forestry income(8) according by the itemized reports of the business expense investments. Second, the result of F-test(One Way ANOVA) for the average analysis by types showed that 3 out of 6 demands for the operation were significant differences and a supplementation necessity of the program was identified significant differences in p<0.01 level. The necessity of forestry resources use and sprawling development prevention are also showed significant differences among types in p<0.05 level. Third, F-test results from 7 questions of desired effects through mountain village creation project revealed that promotion and market security of forestry products and local patriotism instillation through a personal exchange are significant differences between types in p<0.05 level. Forth, the results of duplication benefits(the mountain village development projects and the village support programs with other departments) on the residents' development expectation found that when a village received a large number of projects, residents' expected effects were higher than other village residents where received relatively a small number of projects. However, the expected effects from the increasing quantities of projects were decreased.

Parallel CT image reconstruction based on GPUs

In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions from a small number of projections. However, in practice, these methods are not widely used due to the high computational cost of their implementation. Nowadays technology provides the possibility to reduce effectively this drawback. It is the goal of this work to develop a fast GPU-based algorithm to reconstruct high quality images from under sampled and noisy projection data.

Hull-voxel reconstruction of images as a means of structural analysis

Conventional X-ray tomography is an effective tool of medical diagnostics, nondestructive testing of engineering structures, and technical diagnostics of manufactured products. Tomographic reconstruction of objects is a strongly incorrect inverse problem in the case of limited vision angle, small number of projections, and/or insufficient X-ray source energy. Hull and hull-voxel methods of tomographic reconstruction of piecewise-uniform objects are considered in this paper. These approaches are quite effective for image reconstruction of industrial and biological objects under conditions of sparse input data and for analyzing binary and piecewise-uniform images. It will be a priori assumed that the object being reconstructed consists of areas with constant attenuation coefficients.

Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks

Image reconstruction from a small number of projections is a challenging problem in tomography. Advanced algorithms that incorporate prior knowledge can sometimes produce accurate reconstructions, but they typically require long computation times. Furthermore, the required prior knowledge can be very specific, limiting the type of images that can be reconstructed. Here, we present a reconstruction method that automatically learns prior knowledge using an artificial neural network. We show that this method can be viewed as a combination of filtered backprojection steps, and, therefore, has a relatively low computational cost. Results for two different cases show that the new method is able to use the learned information to produce high quality reconstructions in a short time, even when presented with a small number of projections.

Enhancement of four-dimensional cone-beam computed tomography by compressed sensing with Bregman iteration

In four-dimensional (4D) cone-beam computed tomography (CBCT), there is a spatio-temporal tradeoff that currently limits the accuracy. The aim of this study is to develop a Bregman iteration based formalism for high quality 4D CBCT image reconstruction from a limited number of low-dose projections. The 4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping the projection images corresponding to the phases. To maximally utilize the information from the under-sampled projection data, a compressed sensing (CS) method with Bregman iterations is employed for solving each subproblem. We formulate an unconstrained optimization problem based on least-square criterion regularized by total-variation. The least-square criterion reflects the inconsistency between the measured and the estimated line integrals. Furthermore, the unconstrained problem is updated and solved repeatedly by Bregman iterations. The performance of the proposed algorithm is demonstrated through a series of simulation studies and phantom experiments, and the results are compared to those of previously implemented compressed sensing technique using other gradient-based methods as well as conventional filtered back-projection (FBP) results. The simulation and experimental studies have shown that artifact suppressed images can be obtained with as small as 41 projections per phase, which is adequate for clinical 4D CBCT reconstruction. With such small number of projections, the conventional FDK failed to yield meaningful 4D CBCT images, and CS technique using conjugate gradient was not able to recover sharp edges. The proposed method significantly reduces the radiation dose and scanning time to achieve the high quality images compared to the 4D CBCT imaging based on the conventional FDK technique and the existing CS techniques.

CT Image Reconstruction Based on GPUs

In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections. However, in practice, these methods are not widely used due to the high computational cost of their implementation. Nowadays technology provides the possibility to reduce effectively this drawback. It is the goal of this work to develop a fast GPU-based algorithm to reconstruct high quality images from under sampled and noisy projection data.

Successive binary algebraic reconstruction technique: An algorithm for reconstruction from limited angle and limited number of projections decomposed into individual components

Relatively high radiation CT techniques are being widely used in diagnostic imaging raising the concerns about cancer risk especially for routine screening of asymptomatic populations. An important strategy for dose reduction is to reduce the number of projections, although doing so with high image quality is technically difficult. We developed an algorithm to reconstruct discrete (limited gray scale) images decomposed into individual tissue types from a small number of projections acquired over a limited view angle. The algorithm was tested using projection simulations from segmented CT scans of different cross sections including mid femur, distal femur and lower leg. It can provide high quality images from as low as 5-7 projections if the skin boundary of the cross section is used as prior information in the reconstruction process, and from 11-13 projections if the skin boundary is unknown.

A MULTI-VIEW EVALUATION OF THE FINNISH EIA SYSTEM: AN 18-YEAR SUCCESS STORY?

This article reviews how the Finnish EIA legislation and procedures are performing in practice, and how the goals of the EIA Act are reached. The role of EIA in planning and decision-making, and the relationship between the EIA legislation and other pieces of legislation is also evaluated. Overall, the goals of the Finnish EIA legislation have been successfully reached: EIA has achieved a meaningful role as a preventive and participatory environmental management tool. In addition, stakeholders have understood the role of EIA and the scope of the current EIA legislation, which is under no pressure for major amendments. The EIA is applied to a rather small number of projects, but these are highly significant in terms of their environmental impact. Some areas requiring improvement identified, for example, the need to streamline and clarify the relationship between EIA procedure and zoning regulations as well as between EIA and the environmental permit system. The legislation for different sectors could be harmonised so that it would better take into account the broad information base created through EIA.