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Background: Scatter radiation is a traditional problem in cone-beam computed tomography (CBCT). Accordingly, numerous methods have been researched for scatter reduction in terms of software- or hardware-based solutions. The concept of a two-dimensional antiscatter grid (2D-ASG) has been shown to provide a solution to the scatter reduction in CBCT. However, the use of an ASG makes it challenging to use in clinical CBCT systems because it causes Moire artifacts of the image., Purpose: We have developed a Moire-free 2D-ASG that was designed to solve the Moire artifact and the scatter radiation problems. We provide the experimental results pertaining to the image quality measurements from the evaluation of the 2D-ASG compared with those obtained from a one-dimensional antiscatter grid (1D-ASG) and no antiscatter grid (No-ASG) to demonstrate the quantitative extent of the improvements., Methods: The 2D-ASG, fabricated based on a sawing process with a graphite body, was prepared to evaluate image quality improvements. Projection images for Pro-CT MK II phantom were acquired using the CBCT testbed of sample rotation type and reconstructed by Feldkamp-Davis-Kress (FDK) algorithm without any filters. We measured the contrast-to-noise ratio (CNR) and uniformity index for the cupping artifacts of the 2D-ASG, 1D-ASG, and No-ASG cases., Results: 2D-ASG considerably reduced the cupping artifacts owing to the scatter reduction compared with the 1D-ASG and No-ASG cases. The cupping artifacts were reduced by 85% in the 2D-ASG compared with No-ASG, while the cupping artifacts were reduced by 63% in 1D-ASG compared with No-ASG. The 2D-ASG also yielded a CNR improvement. The average CNR improvements for eight insert materials were 47% in the 2D-ASG compared with No-ASG, while the CNR was improved by 36% in the 1D-ASG compared with No-ASG., Conclusions: We demonstrated that the Moire-free 2D-ASG improved the image quality by removing scatter radiation in CBCT compared with 1D-ASG and No-ASG. We believe that the Moire-free 2D-ASG can become one of the effective ways to solve the scatter radiation problem in CBCT images because it provides usability and has the potential to have synergistic effects on other methods, such as bow-tie filter and scatter correction., (© 2023 American Association of Physicists in Medicine.)

Purpose: To describe an automated algorithm to quantify the foveal avascular zone (FAZ), using optical coherence tomography angiography (OCTA), and to compare its performance for diagnosis of diabetic retinopathy (DR) and association with best-corrected visual acuity (BCVA) to that of extrafoveal avascular area (EAA)., Methods: We obtained 3 × 3-mm macular OCTA scans in diabetic patients with various levels of DR and healthy controls. An algorithm based on a generalized gradient vector flow (GGVF) snake model detected the FAZ, and metrics assessing FAZ size and irregularity were calculated. We compared the automated FAZ segmentation to manual delineation and tested the within-visit repeatability of FAZ metrics. The correlations of two conventional FAZ metrics, two novel FAZ metrics, and EAA with DR severity and BCVA, as determined by Early Treatment Diabetic Retinopathy Study (ETDRS) charts, were assessed., Results: Sixty-six eyes from 66 diabetic patients and 19 control eyes from 19 healthy participants were included. The agreement between manual and automated FAZ delineation had a Jaccard index > 0.82, and the repeatability of automated FAZ detection was excellent in eyes at all levels of DR severity. FAZ metrics that incorporated both FAZ size and shape irregularity had the strongest correlation with clinical DR grade and BCVA. Of all the tested OCTA metrics, EAA had the greatest sensitivity in differentiating diabetic eyes without clinical evidence of retinopathy, mild to moderate nonproliferative DR (NPDR), and severe NPDR to proliferative DR from healthy controls., Conclusions: The GGVF snake algorithm tested in this study can accurately and reliably detect the FAZ, using OCTA data at all DR severity grades, and may be used to obtain clinically useful information from OCTA data regarding macular ischemia in patients with diabetes. While FAZ metrics can provide clinically useful information regarding macular ischemia, and possibly visual acuity potential, EAA measurements may be a better biomarker for DR.

A metallic contact eye shield has sometimes been used for eyelid treatment, but dose distribution has never been reported for a patient case. This study aimed to show the shield-incorporated CT-based dose distribution using the Pinnacle system and Monte Carlo (MC) calculation for 3 patient cases. For the artifact-free CT scan, an acrylic shield machined as the same size as that of the tungsten shield was used. For the MC calculation, BEAMnrc and DOSXYZnrc were used for the 6-MeV electron beam of the Varian 21EX, in which information for the tungsten, stainless steel, and aluminum material for the eye shield was used. The same plan was generated on the Pinnacle system and both were compared. The use of the acrylic shield produced clear CT images, enabling delineation of the regions of interest, and yielded CT-based dose calculation for the metallic shield. Both the MC and the Pinnacle systems showed a similar dose distribution downstream of the eye shield, reflecting the blocking effect of the metallic eye shield. The major difference between the MC and the Pinnacle results was the target eyelid dose upstream of the shield such that the Pinnacle system underestimated the dose by 19 to 28% and 11 to 18% for the maximum and the mean doses, respectively. The pattern of dose difference between the MC and the Pinnacle systems was similar to that in the previous phantom study. In conclusion, the metallic eye shield was successfully incorporated into the CT-based planning, and the accurate dose calculation requires MC simulation., (Copyright © 2015 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.)

Motivation: The availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. To provide a fundamental understanding of how the network information is relevant to phenotype-gene associations, we analyze the circular bigraphs (CBGs) in OMIM human disease phenotype-gene association network and MGI mouse phentoype-gene association network, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. BiRW performs separate random walk simultaneously on gene interaction network and phenotype similarity network to explore gene paths and phenotype paths in CBGs of different sizes to summarize their associations as predictions., Results: The analysis of both OMIM and MGI associations revealed that majority of the phenotype-gene associations are covered by CBG patterns of small path lengths, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations. In the experiments on recovering known associations in cross-validations on human disease phenotypes and mouse phenotypes, BiRW effectively improved prediction performance over the compared methods. The constructed global human disease phenome-genome association map also revealed interesting new predictions and phenotype-gene modules by disease classes.

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype-gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype-gene association matrix under the prior knowledge from phenotype similarity network and protein-protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype-gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein-protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.

Motivation: Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based comparative genomic hybridization (arrayCGH) data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein-protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning., Results: We applied HyperPrior to test two arrayCGH datasets and two gene expression datasets for both cancer classification and biomarker identification. On all the datasets, HyperPrior achieved competitive classification performance, compared with SVMs and the other baselines utilizing the same prior knowledge. HyperPrior also identified several discriminative regions on chromosomes and discriminative subnetworks in the PPI, both of which contain cancer-related genomic elements. Our results suggest that HyperPrior is promising in utilizing biological prior knowledge to achieve better classification performance and more biologically interpretable findings in gene expression and arrayCGH data., Availability: http://compbio.cs.umn.edu/HyperPrior, Contact: kuang@cs.umn.edu, Supplementary Information: Supplementary data are available at bioinformatics online.

Background: Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks., Results: Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins., Conclusion: This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.

Motivation: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis., Results: We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets., Availability: Supplementary results and source code are available at http://compbio.cs.umn.edu/Feature_Class., Supplementary Information: Supplementary data are available at Bioinformatics online.

An adaptive sliding mode control (SMC) algorithm is developed for a missile with highly non-linear aerodynamics. Conventional SMC algorithms have inherent robustness to system model errors, but they often induce chattering of the actuator commands. An adaptive switching gain based on the Lyapunov stability theory is proposed to mitigate the chattering in the steady state. The gain varies with respect to the distance from the sliding surface. The controller with the adaptive switching gain also has robustness to system model errors and unexpected changes in aerodynamic coefficients. A control command generation method using the estimated actuator characteristics is proposed. The proposed method avoids the control performance degradation caused by actuator fault or model disagreement. The effectiveness of the proposed control algorithm is verified through numerical simulations. [ABSTRACT FROM AUTHOR]

In Chu et al. (2004), an efficient structure-preserving doubling algorithm (SDA) was proposed for the solution of discrete-time algebraic Riccati equations (DAREs). In this paper, we generalize the SDA to the G-SDA, for the generalized DARE: E T XE   =   A T XA   -  ( A T XB ...+ C TS )( R   +   B T XB ) -1 ( B T XA   +   S TC )  +   C T QC . Using Cayley transformation twice, we transform the generalized DARE to a DARE in a standard symplectic form without any explicit inversions of (possibly ill-conditioned) R and E . The SDA can then be applied. Selected numerical examples illustrate that the G-SDA is efficient, out-performing other algorithms. [ABSTRACT FROM AUTHOR]

4D-PTV system was constructed. The measurement system consists of three high-speed high-definition cameras (1k × 1k, 2000 fps), Nd-Yag laser (2000 Hz) and a host computer. The GA-3D-PTV algorithm was used for completing the measurement system. The 4D-PTV is capable of probing the spatial distribution of velocity vectors of the flow field overcoming the temporal resolution of the characteristic turbulence length scales of the measured flow fields. A horizontal impinged jet flow (HID = 7) was measured. The Reynolds number is about 33,000. Spatial temporal evolution of the jet flow was examined and physical properties such as spatial distributions of vorticity and turbulent kinetic energy were obtained with the constructed system. [ABSTRACT FROM AUTHOR]