Projection Domain Research Articles

Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain

Optical diffraction tomography (ODT) produces a three-dimensional distribution of the refractive index (RI) by measuring scattering fields at various angles. Although the distribution of the RI is highly informative, due to the missing cone problem stemming from the limited-angle acquisition of holograms, reconstructions have very poor resolution along the axial direction compared to the horizontal imaging plane. To solve this issue, we present a novel unsupervised deep learning framework that learns the probability distribution of missing projection views through an optimal transport-driven CycleGAN. The experimental results show that missing cone artifacts in ODT data can be significantly resolved by the proposed method.

Multi-Feature 360 Video Quality Estimation

We propose a new method for the visual quality assessment of 360-degree (omnidirectional) videos. The proposed method is based on computing multiple spatio-temporal objective quality features on viewports extracted from 360-degree videos. A new model is learnt to properly combine these features into a metric that closely matches subjective quality scores. The main motivations for the proposed approach are that: 1) quality metrics computed on viewports better captures the user experience than metrics computed on the projection domain; 2) the use of viewports easily supports different projection methods being used in current 360-degree video systems; and 3) no individual objective image quality metric always performs the best for all types of visual distortions, while a learned combination of them is able to adapt to different conditions. Experimental results, based on both the largest available 360-degree videos quality dataset and a cross-dataset validation, demonstrate that the proposed metric outperforms state-of-the-art 360-degree and 2D video quality metrics.

SPECIAL: Single-Shot Projection Error Correction Integrated Adversarial Learning for Limited-Angle CT

Limited-angle CT is an indispensable tool for some practical applications when the projection data can be only collected within a limited-angle range due to the constraints of scanning conditions. However, the limited-angle scanning mode will lead to severely degraded images with excessive artifacts. Meanwhile, existing methods fail to reconstruct satisfactory images in limited-angle CT because of the unguaranteed measurement consistency caused by serious projection missing. In this paper, we developed a method termed Single-shot Projection Error Correction Integrated Adversarial Learning (SPECIAL) progressive-improvement strategy, which could effectively combine the complementary information contained in the image domain and projection domain, and greatly improve the reconstructions at the expense of small computational cost. Specifically, enhanced adversarial learning is used in different stages to remove artifacts without losing high-frequency component. A projection error correction module is used to boost the performance in high-attenuation tissue restoration. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method could make a promising improvement on artifact removal, edge preservation and tiny structure restoration.

Application of Hilbert Analysis in Orthogonal Fourier Fringe-projection to Improve Object Shape Reconstruction -=SUP=-*-=/SUP=-

Three-dimensional (3D) measurement of an object is widely used in many fields including machine vision, quality control, robotics, medical diagnostics, and others. High-precision 3D surface topography is necessary for describing object shape accurately with high spatial resolution. A combined approach to improve 3D object shape recovery based on Fourier orthogonal fringe projection together with Hilbert transform is proposed and demonstrated. This new idea of combination is highly effective due to the suppressing of background intensity of the deformed fringe pattern while the zero spectrum is extracted precisely and easily. Removing the zero order component leads to increase the visualization and resolution of the measured object. Application of Hilbert processing for object shape recovery in orthogonal Fourier projection domain to improve 3D visualization has not been reported before. The processing framework of this strategy is described in detail. Validation of the proposed method is verified by experiments including visualization of objects with various shapes and sizes. A comparison between profilometry methods is also given which verify better performance in reconstruction of complex objects. 3D reconstruction of flow running at different speeds on a scattering medium with this combined approach is also demonstrated for the first time. Keywords: 3D shape measurements, orthogonal fringes, Fourier and Hilbert transform, image processing.

Zero-Shot Learning via Discriminative Dual Semantic Auto-Encoder

Zero-shot learning (ZSL) is an effective method to perform the recognition task without any training samples of specific classes. Most existing ZSL models put emphasis on learning an embedding between visual space and semantic space directly. However, few ZSL models research whether the human-designed semantic features are discriminative enough to recognize different classes. Moreover, one-way mapping suffers from the project domain shift problem. In this article, we propose to learn a Discriminative Dual Semantic Auto-encoder (DDSA) based on the encoder-decoder paradigm to solve this problem. DDSA attempts to construct two bidirectional embeddings to connect the visual space and the semantic space with the help of the learned aligned space which includes discriminative information of the visual features and semantic features. Based on the DDSA, we additionally propose a Deep DDSA to capture deep aligned features that are more conducive to zero-shot classification. The key to the proposed framework is that it implicitly exact the principal information from visual space and semantic space to construct aligned features, which is not only semantic-preserving but also discriminative. Extensive experiments on five benchmarks (SUN, CUB, AWA1, AWA2 and aPY) demonstrate the effectiveness of the proposed framework with state-of-the-art performance obtained on both conventional ZSL and generalized ZSL settings.

Feasibility Assessment Framework (FAF): A Systematic and Objective Approach for Assessing the Viability of a Project

Abstract The objective of this article is to discuss the Feasibility Analysis Framework (FAF) as an effective approach to conducting a feasibility study for producing a robust project proposal. The approach is based on key information provided in a project business case developed using the Project Domain Framework (PDF). The PDF is as an alternative model in project management that systematically and logically facilitates the writing of a business case at the project initiation phase. To complement the development of a proposal the FAF has been developed to systematically and objectively produce an effective feasibility study. The FAF is a six step framework, developed using action research strategy that involved over 20 client assignments for a period of five years. The article discusses its concepts and application using a simple but practical real case study. Using the case study it demonstrates how project alternatives and options may be generated, objectively analyzed using weighted the TELOS criteria to confirm the viability of the project by determining whether it will overcome legal, technical, economic and operational requirements in a desired time limit. The process culminates in the accept or reject recommendation.

X-Ray Multispectrum CT Imaging by Projection Sequences Blind Separation Based on Basis-Effect Decomposition

Multispectral CT is a promising method in material characterization, nondestructive evaluation, and other applications. For multispectral CT, the method of projection domain separation can make multispectral CT reconstruction less complex and easier to implement. But X-ray spectrum should be known. By blind separation of projection sequences with different X-ray energy, the ray spectrum is not known in advance, and narrow-spectrum projections can be captured too. However, the proposed blind separation is a signal processing method, without energy constraint. The energy directivity and component characterization accuracy are weaker. To address this problem, this article uses X-ray basis-effect decomposition to propose a new separation model. Both simulation and actual samples are tested to verify and evaluate the proposed method. The experimental results show that the proposed separation model has stronger energy spectrum directivity. The reconstructed attenuation coefficient has smaller error and superior narrow-spectrum characteristics.

Domain-aware Stacked AutoEncoders for zero-shot learning

Zero-shot learning (ZSL), which focuses on transferring the knowledge from the seen (source) classes to unseen (target) ones, is getting more and more attention in the computer vision community. However, there often has a large domain gap between the source and target classes, resulting in the projection domain shift problem. To this end, we propose a novel model, named Domain-aware Stacked AutoEncoders (DaSAE), that consists of two interactive stacked auto-encoders to learn the domain-aware projections for adapting source and target domains respectively. In each of them, the first-layer encoder aims to project a visual feature vector into the semantic space, and the second-layer encoder connects the semantic description of a sample with its label directly. Meanwhile, the two-layer decoders seek to reconstruct the visual representation from the label information and semantic description successively. Moreover, the manifold regularization that explores the manifold structure residing in the target data is integrated to the basic DaAE, which further improves the generalization ability of our model. Extensive experiments on the benchmark datasets clearly demonstrate that our DaSAE outperforms the state-of-the-art alternatives by the significant margins.

A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT

Helical CT has been widely used in clinical diagnosis. In this work, we focus on a new prototype of helical CT, equipped with sparsely spaced multidetector and multi-slit collimator (MSC) in the axis direction. This type of system can not only lower radiation dose, and suppress scattering by MSC, but also cuts down the manufacturing cost of the detector. The major problem to overcome with such a system, however, is that of insufficient data for reconstruction. Hence, we propose a deep learning-based function optimization method for this ill-posed inverse problem. By incorporating a Radon inverse operator, and disentangling each slice, we significantly simplify the complexity of our network for 3D reconstruction. The network is composed of three subnetworks. Firstly, a convolutional neural network (CNN) in the projection domain is constructed to estimate missing projection data, and to convert helical projection data to 2D fan-beam projection data. This is follwed by the deployment of an analytical linear operator to transfer the data from the projection domain to the image domain. Finally, an additional CNN in the image domain is added for further image refinement. These three steps work collectively, and can be trained end to end. The overall network is trained on a simulated CT dataset based on eight patients from the American Association of Physicists in Medicine (AAPM) Low Dose CT Grand Challenge. We evaluate the trained network on both simulated datasets and clinical datasets. Extensive experimental studies have yielded very encouraging results, based on both visual examination and quantitative evaluation. These results demonstrate the effectiveness of our method and its potential for clinical usage. The proposed method provides us with a new solution for a fully 3D ill-posed problem.

A systematic mapping study on architectural smells detection

The recognition of the need for high-quality software architecture is evident from the increasing trend in investigating architectural smells. Detection of architectural smells is paramount because they can seep through to design and implementation stages if left unidentified. Many architectural smells detection techniques and tools are proposed in the literature. The diversity in the detection techniques and tools suggests the need for their collective analysis to identify interesting aspects for practice and open research areas. To fulfill this, in this paper, we unify the knowledge about the detection of architectural smells through a systematic mapping study. We report on the existing detection techniques and tools for architectural smells to identify their limitations. We find there has been limited investigation of some architectural smells (e.g., micro-service smells); many architectural smells are not detected by tools yet; and there are limited empirical validations of techniques and tools. Based on our findings, we suggest several open research problems, including the need to (1) investigate undetected architectural smells (e.g., Java package smells), (2) improve the coverage of architectural smell detection across architecture styles (e.g., service-oriented and cloud), and (3) perform empirical validations of techniques and tools in industry across different languages and project domains.

Fourier Properties of Symmetric-Geometry Computed Tomography and Its Linogram Reconstruction With Neural Network.

In this work, we investigate the Fourier properties of a symmetric-geometry computed tomography (SGCT) with linearly distributed source and detector in a stationary configuration. A linkage between the 1D Fourier Transform of a weighted projection from SGCT and the 2D Fourier Transform of a deformed object is established in a simple mathematical form (i.e., the Fourier slice theorem for SGCT). Based on its Fourier slice theorem and its unique data sampling in the Fourier space, a Linogram-based Fourier reconstruction method is derived for SGCT. We demonstrate that the entire Linogram reconstruction process can be embedded as known operators into an end-to-end neural network. As a learning-based approach, the proposed Linogram-Net has capability of improving CT image quality for non-ideal imaging scenarios, a limited-angle SGCT for instance, through combining weights learning in the projection domain and loss minimization in the image domain. Numerical simulations and physical experiments on an SGCT prototype platform showed that our proposed Linogram-based method can achieve accurate reconstruction from a dual-SGCT scan and can greatly reduce computational complexity when compared with the filtered backprojection type reconstruction. The Linogram-Net achieved accurate reconstruction when projection data are complete and significantly suppressed image artifacts from a limited-angle SGCT scan mimicked by using a clinical CT dataset, with the average CT number error in the selected regions of interest reduced from 67.7 Hounsfield Units (HU) to 28.7 HU, and the average normalized mean square error of overall images reduced from 4.21e-3 to 2.65e-3.

Role of Phosphorylation and Hyperphosphorylation of Tau in Its Interaction with βα Dimeric Tubulin Studied from a Bioinformatics Perspective.

Background:Tau is a disordered Microtubule Associated Protein (MAP) which prefers to bind and stabilize microtubules. Phosphorylation of tau in particular enhances tautubulin interaction which otherwise detaches from tubulin during hyperphosphorylation. The reason behind their destabilization, detachment and the role of β subunit (from microtubule) and the projection domain (Tau) in microtubule stability remains elusive till date. Thus, a complete 3D structural investigation of tau protein is much needed to address these queries as the existing crystal structures are in fragments and quite limited.Methods:In this study, the modelled human tau protein was subjected to phosphorylation and hyperphosphorylation which were later considered for docking with micro-tubules (βα subunits-inter dimer) and vinblastine.Results:Phosphorylated tau protein interacts with both α- and β subunits. But stronger bonding was with α- compared to β subunits. Regarding β subunit, proline rich loop and projection domain actively participated in tau binding. Interestingly, hyperphosphorylation of tau increases MAP domain flexibility which ultimately results in tau detachment, the main reason behind tangle formation in Alzheimer’s disease.Conclusion:This study being the first of its kind emphasizes the role of projection domain and proline rich region of β-subunit in stabilizing the tau-tubulin interaction and also the effect of hyperphosphorylation in protein-protein and protein-drug binding.

Advancing integrated care evaluation in shifting contexts: blending implementation research with case study design in project SUSTAIN

BackgroundDespite many studies evaluating the effectiveness of integrated care, evidence remains inconsistent. There is increasing commentary pointing out the mismatch between the ability to capture the somewhat ‘illusive’ impact of integrated care initiatives and programmes, and the most appropriate way to do this. Focusing on methodology, this paper describes and critically reviews the experiences of SUSTAIN, a Horizon 2020 funded project (2015–2019) with the purpose of advancing knowledge and understanding of cross-European integrated care evaluation. SUSTAIN sought to improve integrated care initiatives for older people in seven countries, and to maximise the potential for knowledge transfer and application across Europe. The methods approach drew from implementation research, employing the participative Evidence Integration Triangle (EIT) and incorporating a mixed method, multiple embedded case study design. A core set of qualitative and quantitative indicators, alongside context and process data, were created and tested within four key project domains (person-centredness, prevention-orientation, safety and efficiency). The paper critically discusses the overall approach, highlighting the value of the EIT and case study design, and signalling the challenges of data collection with frail older people and stakeholder involvement at the sites, as well as difficulties developing the core set of indicators.ConclusionsLessons learned and recommendations for advancing integrated care evaluation are put forward that focus on the status of integrated care as a complex intervention and a process. The use of implementation research methods and case study design are recommended as an additional evaluation approach for researchers to consider, alongside suggested ways of improving methods of data collection with frail populations and cost analysis.

Domain focused and residue focused phosphorylation effect on tau protein: A molecular dynamics simulation study

Phosphorylation has been hypothesized to alter the ability of tau protein to bind with microtubules (MT), and pathological level of phosphorylation can incorporate formation of Paired Helical Filaments (PHF) in affected tau. Study of the effect of phosphorylation on different domains of tau (projection domain, microtubule binding sites and N-terminus tail) is important to obtain insight about tau neuropathology. In an earlier study, we have already obtained the mechanical properties and behavior of single tau and dimerized tau and observed tau-MT interaction for normal level of phosphorylation. This study attempts to obtain insights on the effect of phosphorylation on different domains of tau, using molecular dynamics (MD) simulation with the aid of CHARMM force field under high strain rate. It also determines the effect of residue focused phosphorylation on tau-MT interaction and tau accumulation tendency. The results show that for single tau protein, unfolding stiffness does not differ significantly due to phosphorylation, but stretching stiffness can be much higher than the normally phosphorylated protein. For dimerized tau protein, the stretching required to separate the protein forming the dimer is similar for phosphorylation in individual domains but is significantly less in case of phosphorylation in all domains. For tau-MT interaction simulations, it is found that for normal phosphorylation, the tau separation from MT occurs at higher strain for phosphorylation in projection domain and N-terminus tail, and earlier for phosphorylation in all domains altogether than the normal phosphorylation state. The residue focused phosphorylation study also shows that tau separates earlier from MT and shows stronger accumulation tendency at the phosphorylated state, while preserving the highly stretchable and flexible characteristic of tau. This study provides important insight on mechanochemical phenomena relevant to traumatic brain injury (TBI) scenario, where the result of mechanical loading and posttranslational modification as well as conformation decides the mechanical behavior.

Communication Skills: Use of the Interprofessional Communication Curriculum to Address Physical Aspects of Care.

The literature has emphasized the importance of effective communication regarding psychosocial needs; however, other aspects of patient care, including attention to physical needs, are equally important. The aims of this article are to (a) describe an Interprofessional Communication Curriculum (ICC) in oncology, (b) detail the curriculum content specifically focused on physical aspects of care, and (c) illustrate the importance of interprofessional care in oncology. The ICC is organized by the 8 domains of the National Consensus Project for Quality Palliative Care and centers on communication skills needed in oncology clinical practice. Based on initial pilot data, oncology clinicians indicate a high level of satisfaction with the ICC. Additional future training courses supported by the National Cancer Institute will prepare oncology teams to enhance communication with patients and families.

Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship.

Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm. To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100mAs/120kVp) to ultralow dose (1mAs/120kVp) by using quantitative texture-based evaluation metrics. Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7×7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.

An IT governance framework for IS portfolio management

PurposeSuccess in the information systems (IS) project domain is elusive despite extensive research on the topic. Governance is seen as the greatest contributor to project success. The purpose of this paper is to investigate and report on the current perceptions and implementation of information technology (IT) governance within IS portfolio management to develop a sub-framework to guide practitioners. This sub-framework forms part of a grand IS project, programme and portfolio governance framework of which this study forms a contributing part.Design/methodology/approachThe researchers followed a mixed-methods approach through utilising Q-methodology and inverted factor analysis.FindingsThe results provided a sub-framework recommending specific IT governance practices to be applied to IS portfolios. The recommendations are categorised as activities to be maintained, enhanced and/or implemented.Research limitations/implicationsThe research only had participants from South African organisations and as such cannot be reliably extrapolated to other regions.Originality/valueThe resultant sub-framework provides stakeholders and practitioners involved in IS portfolios an opportunity to examine their own approaches and be confronted with possibilities in their portfolio management activities. Further research to be conducted includes creating a grand framework to address the linkages between portfolio, programme and project management as it relates to IT governance on various strategic levels.

Update on Multienergy CT: Physics, Principles, and Applications.

Multienergy CT involves acquisition of two or more CT measurements with distinct energy spectra. Using the differential attenuation of tissues and materials at different x-ray energies, multienergy CT allows distinction of tissues and materials beyond that possible with conventional CT. Multienergy CT technologies can operate at the source or detector level. Dual-source, rapid tube-voltage switching, and dual-layer detector CT are the most commonly used multienergy CT technologies. Most of the currently available technologies typically use two energy levels, commonly referred to as dual-energy CT. With use of two or more energy bins, photon-counting detector CT can perform multienergy CT beyond current dual-energy CT technologies. Multienergy CT postprocessing can be performed in the projection or image domain using two-material or multimaterial decomposition. The most commonly used multienergy CT images are virtual monoenergetic images (VMIs), iodine maps, virtual noncontrast (VNC) images, and uric acid images. Low-energy VMIs are used to boost contrast signal and enhance lesion conspicuity. High-energy VMIs are used to decrease some artifacts. Iodine maps are used to evaluate perfusion, characterize lesions, and evaluate response to therapy. VNC images are used to characterize lesions and save radiation dose by eliminating true noncontrast images from multiphasic acquisitions. Uric acid images are used for characterization of renal calculi and gout. Online supplemental material is available for this article. ©RSNA, 2020.

The Missing Link in Project Governance: Permeability and Influence of Governance Precepts on Decision Making in the Project Domain

This study aims to understand how the information about corporate governance precepts permeates the organization within the project environment and influences decision making. After analyzing 157 decisions, we concluded that there is a lack of robustness in the governance mechanisms to convey this information consistently. However, when there is strength to break the gap and reach the agent, the decisions made in the projects are 16.5 times more likely to be aligned with corporate governance. This finding reveals the importance of effective and consistent project governance mechanisms.

Collective resistance intention of migrants to resettlement policies: the role of influential pathways of information sources

The failure of large-scale resettlement policies has been largely determined by the collective resistance of residents. For migrants who must leave their original residence and get resettled, mass media and interpersonal communication are important sources to get policy information, which affects their attitudes and responses to resettlement policies. Existing studies have provided two pathways to explain the influencing mechanism between information sources and people’s collective resistance intention. One of these pathways focuses on trust in government and the other highlights perceived benefits. However, it is still unclear how to predict migrants’ collective resistance intention based on different information sources within these two pathways. By concentrating on the policy domain of Chinese resettlement projects, this study aims to develop an integrated, theoretical framework to explore the causal paths between information sources and migrants’ collective resistance intention. Based on a survey of three towns in Ankang, China, we tested the causal mechanism with a structural equation model. The results demonstrate that the pathway that involves trust in government is more explanatory in illustrating the causal mechanism than the pathway of perceived benefits, and interpersonal communication yields stronger effects than mass media in mitigating migrants’ collective resistance intention. The implications for resettlement policy publicity and trust-building in China are discussed. Points for practitioners Public communication is a critical competency for public managers. Public managers are recommended to flexibly apply various information sources to communicate with migrants in order to eventually mitigate their collective resistance intention by strengthening their trust in government, as well as their perceived benefits. In the early stages of policy publicity, a variety of mass media are needed to inform the migrants of resettlement policies. Then, at the stage of policy implementation, the primary focus becomes interpersonal communication that offers sufficiently detailed information. In this regard, local government managers should carry out more programs that entail face-to-face information sharing to help participants understand the resettlement policy, and encourage them to explain it to their family and friends.