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X-ray induced acoustic computed tomography for nondestructive testing of aircraft structure

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X-ray induced acoustic computed tomography for nondestructiv...

ASME 2019 International Mechanical engineering Congress and Exposition, IMECE 2019

作者： Tran, Tiffany Samant, Pratik Xiang, Liangzhong Liu, Yingtao School of Aerospace and Mechanical Engineering University of Oklahoma NormanOK73019 United States Stephenson School of Biomedical Engineering University of Oklahoma NormanOK73019 United States School of Electrical and Computer Engineering University of Oklahoma NormanOK73019 United States

ISBN: (纸本)9780791859360

For decades, aircraft disasters have always been a concern for airline companies and especially for consumers. Scientists all over the world have been constantly trying to study, discover and invent new methods for testing and prevention to reduce future aircraft accidents. One of those methods is non-destructive testing, which is a widely adaptive process for analyzing structural integrity over wide arrays of object. X-rays, ultrasound and computed tomography (CT) are non-destructive testing applications commonly used for the commercial aircraft maintenance. These non-destructive testing methods for aircraft structures give us high-quality images of structural damage but, there are some disadvantages related to resolution and the contrast mechanism of the image. The goal of this study is to demonstrate the concept of X-Ray Induced Acoustic Computed Tomography (XACT) imaging method for defect detection and localization through simulations using k-wave MATLAB toolbox. XACT is a technique based on the X-ray induced acoustic effect. In XACT, a short pulsed of X-rays are required to achieve thermal response and generate acoustic waves. X-ray travels to an object, the photons are absorbed causing the temperature in the object to raise, which generates acoustic waves due to thermoelastic expansion. These acoustic waves are then detected by ultrasonic transducers. Within the fuselage of the aircraft, the aircraft’s stiffener is designed using SolidWorks. along with two different types of defects through voids due to manufacturing imperfection process. As well as, cracks in the surface of the model due to mechanical failures are created in MATLAB. Two properties of Aluminum 6065 and Inconel 625 materials were selected for our simulation study since it is often used for the fuselage and/or aircraft engines. XACT images are generated under the combination of high X-ray absorption and ultrasonic transducers that will be able to overcome the disadvantages of the X-ray imaging t

关键词： Nondestructive examination

AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

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arXiv 2025年

作者： Jabin, Md. Asaduzzaman Jiang, Hanqi Li, Yiwei Kaggwa, Patrick Douglass, Eugene Sekandi, Juliet N. Liu, Tianming School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA30602 United States School of Computing College of Engineering University of Georgia AthensGA30602 United States Department of Epidemiology and Biostatistics College of Public Health University of Georgia AthensGA30602 United States Department of Pharmaceutical and Biomedical Sciences College of Pharmacy University of Georgia AthensGA30602 United States Global Health Institute College of Public Health University of Georgia AthensGA30602 United States

Chronic diseases, including diabetes, hypertension, asthma, HIV/AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to avert disease progression, manage symptoms, and decrease mortality rates. Adherence is frequently undermined by factors including patient behavior, caregiver support, elevated medical costs, and insufficient healthcare infrastructure. We propose AdCare-VLM, a specialized Video-LLaVA-based multimodal large vision language model (LVLM) aimed at visual question answering (VQA) concerning medication adherence through patient videos. We employ a private dataset comprising 806 custom-annotated tuberculosis (TB) medication monitoring videos, which have been labeled by clinical experts, to fine-tune the model for adherence pattern detection. We present LLM-TB-VQA, a detailed medical adherence VQA dataset that encompasses positive, negative, and ambiguous adherence cases. Our method identifies correlations between visual features—such as the clear visibility of the patient’s face, medication, water intake, and the act of ingestion—and their associated medical concepts in captions. This facilitates the integration of aligned visual-linguistic representations and improves multimodal interactions. Experimental results indicate that our method surpasses parameter-efficient fine-tuning (PEFT) enabled VLM models, such as LLaVA-V1.5 and Chat-UniVi, with absolute improvements ranging from 3.1% to 3.54% across pre-trained, regular, and low-rank adaptation (LoRA) configurations. Comprehensive ablation studies and attention map visualizations substantiate our approach, enhancing interpretability. © 2025, CC BY-NC-ND.

关键词： Fetal monitoring

Current and future directions in network biology

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

Robust Intrusion Detection in Dynamic Networks

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Robust Intrusion Detection in Dynamic Networks

Control Technology and Applications (CCTA),

作者： Sam Nazari Bahram Shafai Anahita Moradmand Department of Electrical and Computer Engineering Northeastern University Boston MA USA Electrical and Computer Engineering Biomedical Engineering Northeastern University Boston MA USA

This paper considers the problem of robustly identifying m intruders in a network consisting of n cooperative agents which are subject to unknown disturbances. First, a distributed system model is introduced so that the relationship between agents, the attacks and unknown disturbances can be captured. Next, the distributed identification scheme is formulated as a spectral assignment problem and necessary filter gains are obtained through a carefully constructed linear system of equations. Necessary and sufficient conditions to decouple the unknown disturbances from the agents residual generators are derived in terms of filter gain matrices. It is shown that the problem of discriminating between unknown disturbances and attacks in a distributed system under consensus dynamics can be reduced to the problem of determining a set of constraints on the spectrum of the residual generator coefficient matrices. The approach is illustrated through an example.

关键词： Monitoring Mathematical model Robustness Observers Nickel Generators Laplace equations

Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology

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arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Newlin, Nancy R. Saunders, Adam M. Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Purpose: Cells are building blocks for human physiology;consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues. Microscope slides of H&E-stained tissues are widely available in both clinical and research settings. While H&E is ubiquitous and reveals tissue microanatomy, the classification and tracking of cell subtypes often requires expert knowledge and the use of specialized stains, such as immunofluorescence staining. To reduce both the manual annotation burden and reliance on specialized staining technologies, artificial intelligence has been proposed for the automatic classification of cell types on H&E slides. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on imaging of H&E stains from the human colon. However, this is a very small fraction of the number of potential cell subtypes within the intestines. Specifically, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). To approach this problem, we propose to use inter-modality learning to label previously un-labelable cell types on H&E. Approach: We take advantage of the cell classification information inherent in whole slide images (WSIs) of multiplexed immunofluorescence (MxIF) histology to create cell level annotations for 14 subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on a testing

关键词： Tissue

A Direct Algebraic Approach to Design State Feedback and Observers for Singular Systems

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A Direct Algebraic Approach to Design State Feedback and Obs...

Control Technology and Applications (CCTA),

作者： Bahram Shafai Sam Nazari A. Moradmand Electrical and Computer Engineering Biomedical Engineering at Northeastern University Boston MA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

This paper considers the stabilization problem of singular systems represented by an equivalent system involving input derivatives. Stabilization is achieved by eigenvalue assignment after a subsequent elimination process of input derivatives. First, the connection between the class of systems with derivative inputs and singular systems is analyzed. Then an elimination procedure for input derivatives is given, which transfers the derivative terms from the state equation to the output equation allowing stabilization by state feedback to be performed under a modified controllability condition. The design of observer for the transformed system with input derivatives appearing in the output equation is also considered. It is shown that a similar elimination process can be performed directly in the design procedure of the observer. An immediate benefit of the new approach is the fact that it can be employed to systems that naturally admit input derivatives in their state space models. This is further elaborated in the paper and illustrated as part of design examples.

关键词： Observers Linear systems State feedback Mathematical model Standards Controllability

AGMB-Transformer: Anatomy-guided multi-branch transformer network for automated evaluation of root canal therapy

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arXiv 2021年

作者： Li, Yunxiang Zeng, Guodong Zhang, Yifan Wang, Jun Jin, Qun Sun, Lingling Zhang, Qianni Lian, Qisi Qian, Guiping Xia, Neng Peng, Ruizi Tang, Kai Wang, Shuai Wang, Yaqi Microelectronics CAD Center Hangzhou Dianzi University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland National Clinical Research Center for Oral Diseases West China Hospital of Stomatology Sichuan University Chengdu China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom College of Media Engineering Communication University of Zhejiang Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients’ treatment outcome. Nowadays, the evaluation is performed in a manual manner, which is time-consuming, subjective, and error-prone. In this paper, we aim to automate this process by leveraging the advances in computer vision and artificial intelligence, to provide an objective and accurate method for root canal therapy result assessment. A novel anatomy-guided multi-branch Transformer (AGMB-Transformer) network is proposed, which first extracts a set of anatomy features and then uses them to guide a multi-branch Transformer network for evaluation. Specifically, we design a polynomial curve fitting segmentation strategy with the help of landmark detection to extract the anatomy features. Moreover, a branch fusion module and a multi-branch structure including our progressive Transformer and Group Multi-Head Self-Attention (GMHSA) are designed to focus on both global and local features for an accurate diagnosis. To facilitate the research, we have collected a large-scale root canal therapy evaluation dataset with 245 root canal therapy X-ray images, and the experiment results show that our AGMB-Transformer can improve the diagnosis accuracy from 57.96% to 90.20% compared with the baseline network. The proposed AGMB-Transformer can achieve a highly accurate evaluation of root canal therapy. To our best knowledge, our work is the first to perform automatic root canal therapy evaluation and has important clinical value to reduce the workload of endodontists. © 2021, CC BY.

关键词： Patient treatment

ADAPTIVE AGGREGATION WEIGHTS FOR FEDERATED SEGMENTATION OF PANCREAS MRI

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arXiv 2024年

作者： Pan, Hongyi Durak, Gorkem Zhang, Zheyuan Taktak, Yavuz Keles, Elif Aktas, Halil Ertugrul Medetalibeyoglu, Alpay Velichko, Yury Spampinato, Concetto Schoots, Ivo Bruno, Marco J. Keswani, Rajesh N. Tiwari, Pallavi Bolan, Candice Gonda, Tamas Goggins, Michael G. Wallace, Michael B. Xu, Ziyue Bagci, Ulas Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Internal Medicine Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Radiology and Nuclear Medicine Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Northwestern University ChicagoIL United States Department of Biomedical Engineering and Radiology University of Wisconsin MadisonWI United States Division of Gastroenterology and Hepatology Mayo Clinic JacksonvilleFL United States Division of Gastroenterology and Hepatology New York University New YorkNY United States Division of Gastroenterology and Hepatology Johns Hopkins University BaltimoreMD United States NVIDIA BethesdaMD United States

Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers). © 2024, CC BY.

关键词： Federated learning

NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity

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arXiv 2019年

作者： Batabyal, Tamal Condron, Barry Acton, Scott T. Department of Electrical and Computer Engineering University of Virginia Department of Biology University of Virginia Department of Electrical and Computer Engineering Department of Biomedical Engineering University of Virginia

The shape and connectivity of a neuron determine its function. Modern imaging methods have proven successful at extracting such information. However, in order to analyze this type of data, neuronal morphology needs to be encoded in a graph-Theoretic method. This encoding enables the use of high throughput informatic methods to extract and infer brain function. The application of graph-Theoretic methods to neuronal morphological representation comes with certain difficulties. Here we report a novel, effective method to accomplish this task. The morphology of a neuron, which consists of its overall size, global shape, local branch patterns, and cell-specific biophysical properties, can vary significantly with the cell's identity, location, as well as developmental and physiological state. Various algorithms have been developed to customize shape based statistical and graph related features for quantitative analysis of neuromorphology, followed by the classification of neuron cell types using the features. Unlike the classical feature extraction based methods from imaged or 3D reconstructed neurons, we propose a model based on the rooted path decomposition from the soma to the dendrites of a neuron and extract morphological features on each path. We hypothesize that measuring the distance between two neurons can be realized by minimizing the cost of continuously morphing the set of all rooted paths of one neuron to another. To validate this claim, we first establish the correspondence of paths between two neurons using a modified Munkres algorithm. Next, an elastic deformation framework that employs the square root velocity function is established to perform the continuous morphing, which, in addition, provides an effective visualization tool. We experimentally show the efficacy of NeuroPath2Path, NeuroP2P, over the state of the art. Copyright © 2019, The Authors. All rights reserved.

关键词： Neurons