Terms Of Data Consistency Research Articles

Aggregated motion estimation for real-time MRI reconstruction

In real-time MRI serial images are generally reconstructed from highly undersampled datasets as the iterative solutions of an inverse problem. While practical realizations based on regularized nonlinear inversion (NLINV) have hitherto been surprisingly successful, strong assumptions about the continuity of image features may affect the temporal fidelity of the estimated reconstructions. The proposed method for real-time image reconstruction integrates the deformations between nearby frames into the data consistency term of the inverse problem. The aggregated motion estimation (AME) is not required to be affine or rigid and does not need additional measurements. Moreover, it handles multi-channel MRI data by simultaneously determining the image and its coil sensitivity profiles in a nonlinear formulation which also adapts to non-Cartesian (e.g., radial) sampling schemes. The new method was evaluated for real-time MRI studies using highly undersampled radial gradient-echo sequences. AME reconstructions for a motion phantom with controlled speed as well as for measurements of human heart and tongue movements demonstrate improved temporal fidelity and reduced residual undersampling artifacts when compared with NLINV reconstructions without motion estimation. Nonlinear inverse reconstructions with aggregated motion estimation offer improved image quality and temporal acuity for visualizing rapid dynamic processes by real-time MRI.

Reliability of long-term snow depth data sets from remote sensing over the western arid zone of China

Due to the coarse spatial resolution of imagery and heterogeneity of snow physical parameters, the currently available snow depth data sets derived from passive microwave sensors have shown significant accuracy variations in different parts of the world. This study aims to analyse the reliability of existing remote sensing snow depth products for the arid zone of China. Two long-term products were compared and evaluated including the GlobSnow Snow Water Equivalent product (‘the GlobSnow product’) and the Long-term Snow Depth Dataset of China (‘the WestDC product’). Nine-year ground measurements from 35 sampling sites with diverse topographical conditions were used as ground references for the accuracy assessment. Statistical analysis methods such as intra-class correlation coefficient and root mean square error (RMSE) were adopted for the consistency test and accuracy assessment. Analysis of variance (ANOVA) was employed to examine whether the data reliability varied by seasonal or locational factors. The results show that the two products in general do not agree well in the study region. However, the generalized data with different temporal resolutions tend to yield better results in terms of data consistency and accuracy. Compared to the GlobSnow product, the WestDC product has shown a better accuracy with the RMSE of 8.23, 7.43, and 6.56 cm for the original daily, and generalized weekly, and monthly data, respectively. According to the ANOVA test, season and latitude show statistically significant impacts on data reliability, while altitude and terrain complexity do not. Data reliability declines with higher latitude and it rapidly falls down to an unacceptable level in snow melting periods. This study provides a quantitative assessment on the quality of the snow depth products and raises the awareness of data consistency issues of these products for regional applications.

Blind Compressive Sensing Dynamic MRI

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the nonorthogonal nature of the dictionary basis functions. Since the number of degrees-of-freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting l1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize-minimize algorithm, which decouples the original criterion into three simpler subproblems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the l1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the l0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding. Our phase transition experiments demonstrate that the BCS scheme provides much better recovery rates than classical Fourier-based CS schemes, while being only marginally worse than the dictionary aware setting. Since the overhead in additionally estimating the dictionary is low, this method can be very useful in dynamic magnetic resonance imaging applications, where the signal is not sparse in known dictionaries. We demonstrate the utility of the BCS scheme in accelerating contrast enhanced dynamic data. We observe superior reconstruction performance with the BCS scheme in comparison to existing low rank and compressed sensing schemes.

Combinational usage of next generation sequencing and qPCR for the analysis of tumor samples

The combination of multiple techniques especially those adding complementary information have proven to be beneficial in terms of data consistency. The employment of quantitative PCR (qPCR) prior to next generation sequencing (NGS) methods such as RNA-Seq and mutational analysis presented here does not only enhance data in terms of CNV integration and sample choice, but also allows a faster and more efficient workflow. Correct analysis of libraries prior to sequencing has proven to be a vital step for specific assumption and to some extent for a more parallel testing. By illustrating the combination of qPCR and NGS in oncological examples, the potential of this approach is presented.

A meta-model for knowledge representation in engineering design

Computer Aided Design (CAD) and Computer Aided Engineering (CAE) models are often used during product design. Various interactions between the different models must be managed for the designed system to be robust and to fit initially defined specifications. A lot of data are exchanged in collaborative design and different problems must be managed in terms of data consistency. A literature survey about existing knowledge models is done before presenting the proposed meta-model of knowledge. This meta-model is decomposed in a meta-model of expert knowledge and one of collaboration knowledge in order to represent interaction and link among knowledge between the different expertise of collaborative design.

Why We Still Need HBV Population-Based Epidemiologic Studies

As a practicing physician, I face clinical problems every day. To solve them, I use clinical decision making skills including reaching the most accurate diagnosis and deciding on the next step or steps to manage the problem. A clinician is a pragmatic person; i.e., s/he needs practical, implementable data to guide reasoning or decision making and nothing else! In this environment, why is Mohammad Reza Ghadir's article on the epidemiology of Hepatitis B virus infection in this issue of the journal at all? [1]. Of course it is a Socratic question, and you may find me to be the author of similar articles [2]. Description and research on epidemiology from the aspects of incidence and prevalence will affect pre-test likelihood in clinical decision-making. When a doctor encounters a patient in daily practice, s/he needs to know the possibility of the presence of a condition or disease when a sign or symptom is present; therefore the background epidemiology of the disease in the population that the patient comes from is important. It is especially important when there are limited data regarding the population; for example when there is a demographic shift due to immigration to a new area or a change of transmission-related factors after universal HBV vaccination. In terms of infectious disease, these epidemiologic shifts may be rapid and may necessitate surveillance and trend analysis through point estimation studies. In the context Ghadir reported, there is a dynamic, ever-migrating population and a disease whose epidemiology changes frequently. Epidemiologic studies may help indicate possible risk factors, especially when using methodologies that include attributable risk calculations. The risk factors may be manageable in a clinical setting or may be used by health authorities to plan preventive projects for the community. This approach may be combined with an estimation of risk factor manipulation in the patient as a single case as well as in the community. For example, if there is an established attributable risk of intravenous drug use with needle sharing that could disseminate the infection in the community, you may put an HBV patient on Methadone maintenance therapy (MMT). On the other hand, in a case of monogamous sexual contact with an HBcAb-positive sex partner at the age of 50, you may conclude that there is no need to recommend barrier methods. Along these lines, the risk factor tables in the Ghadir articles attempt to guide us. This is a methodological change and results in a nested case-control type of plan within a large cross-sectional study. When the prevalence of a disease is low and consequently the cases within a sample of population are limited, analyzing risk factors will result in methodological problems. This is the problem that Ghadir encounters in some rows of his table, for example when analyzing tattooing as a risk factor. On other hand, when there is a shared risk factor in the majority of the sample and there is no measurement of higher or lower exposure rates, the researcher cannot conclude that this factor is essentially a risk or not. Consider unsafe barber practices within a male population and its effect on HBV transmission. The last evidence-gathering step before concluding on clinical guidelines would be a decision analysis study. The formal methodology in such studies is to draw a decision tree and conduct reverse calculations. To enable the calculation, probabilities are needed for each arm, and to define the probabilities, it is essential to know the prevalence and incidence of each subgroup, which are extractable from epidemiologic studies. Even when a researcher intends to do a sensitivity analysis as part of the decision analysis, the ideal conclusion will be reached when the epidemiologic variations are known and a systematic review performed, such as that done by Alavian et al. in their publication on HBV epidemiology [3]. In terms of data consistency, a new piece of information on the same HBV epidemiology surely does not add anything to our current preventive and clinical practice; in such circumstances when associated with limited manpower and financial support, resources are better shifted toward other studies. Along with the importance of population based epidemiologic studies, I would like to note that, in terms of scientific reasoning, dissociating such studies from other biomedical studies on infectious transmission and risk factors may result in a reductionist philosophy. Without knowing the etiopathogenesis of the disease, variations in humans from the viewpoint of genomics, proteomics, and even metabolomics will result in pitfalls that have happened throughout the history of medicine.

Reliability of feedback fechanism based on root cause defect analysis - case study

The case study presented in this article focuses on the common pitfall in which many defect prevention activities fall, caused by inconsistency of data. Before any collected Root Cause Defect Analysis (RCA) data can be used reliably, an analysis of the variation due to the measurement system itself such as a Gauge R&R study, should be performed. This allows to understand of the integrity of the data in terms of data consistency and validity. The RCA data collection process chosen for this case study concentrates on the RCA assessment performed by engineers independently.

International Trends in Alcohol Production, Trade and Consumption, and Their Relationship to Alcohol-Related Problems, 1970 to 1977

The intent of this study was to produce updated statistics on worldwide trends in alcohol production, trade and consumption. Statistics were selected from existing data banks maintained by international government organizations and marketing boards, and supplemented by statistics published by individual countries, in order to select the most satisfactory statistical series in terms of data consistency and time period covered. The nature of the data used and some of the problems encountered will be described. In addition, global trends in total and per capita production, trade and consumption of alcohol, by alcohol type, as well as some differences in terms of geographic areas, economic systems and alcoholic beverage preferences will be described. Finally, the worldwide relationship between levels of alcohol consumption and deaths due to liver cirrhosis will be discussed, and there will be a description of how these results can be used as indicators of the level of alcohol-related problems in specific countries.