377 research outputs found

    Imaging Sensors and Applications

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    In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

    Augmented Reality

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    Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

    Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

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    Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed

    Research and technology at Kennedy Space Center

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    As the NASA Center responsible for assembly, checkout, servicing, launch, recovery, and operational support of Space Transportation System elements and payloads, Kennedy Space Center is placing increasing emphasis on the Center's research and technology program. In addition to strengthening those areas of engineering and operations technology that contribute to safer, more efficient, and more economical execution of current mission, the technical tools are developed needed to execute Center's mission relative to future programs. The Engineering Development Directorate encompasses most of the laboratories and other Center resources that are key elements of research and technology program implementation and is responsible for implementation of the majority of the projects in this Kennedy Space Center 1989 Annual Report

    Image-Guided Interventions Using Cone-Beam CT: Improving Image Quality with Motion Compensation and Task-Based Modeling

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    Cone-beam CT (CBCT) is an increasingly important modality for intraoperative 3D imaging in interventional radiology (IR). However, CBCT exhibits several factors that diminish image quality — notably, the major challenges of patient motion and detectability of low-contrast structures — which motivate the work undertaken in this thesis. A 3D–2D registration method is presented to compensate for rigid patient motion. The method is fiducial-free, works naturally within standard clinical workflow, and is applicable to image-guided interventions in locally rigid anatomy, such as the head and pelvis. A second method is presented to address the challenge of deformable motion, presenting a 3D autofocus concept that is purely image-based and does not require additional fiducials, tracking hardware, or prior images. The proposed method is intended to improve interventional CBCT in scenarios where patient motion may not be sufficiently managed by immobilization and breath-hold, such as the prostate, liver, and lungs. Furthermore, the work aims to improve the detectability of low-contrast structures by computing source–detector trajectories that are optimal to a particular imaging task. The approach is applicable to CBCT systems with the capability for general source–detector positioning, as with a robotic C-arm. A “task-driven” analytical framework is introduced, various objective functions and optimization methods are described, and the method is investigated via simulation and phantom experiments and translated to task-driven source–detector trajectories on a clinical robotic C-arm to demonstrate the potential for improved image quality in intraoperative CBCT. Overall, the work demonstrates how novel optimization-based imaging techniques can address major challenges to CBCT image quality

    Metrics for Specification, Validation, and Uncertainty Prediction for Credibility in Simulation of Active Perception Sensor Systems

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    The immense effort required for the safety validation of an automated driving system of SAE level 3 or higher is known not to be feasible by real test drives alone. Therefore, simulation is key even for limited operational design domains for homologation of automated driving functions. Consequently, all simulation models used as tools for this purpose must be qualified beforehand. For this, in addition to their verification and validation, uncertainty quantification (VV&UQ) and prediction for the application domain are required for the credibility of the simulation model. To enable such VV&UQ, a particularly developed lidar sensor system simulation is utilized to present new metrics that can be used holistically to demonstrate the model credibility and -maturity for simulation models of active perception sensor systems. The holistic process towards model credibility starts with the formulation of the requirements for the models. In this context, the threshold values of the metrics as acceptance criteria are quantifiable by the relevance analysis of the cause-effect chains prevailing in different scenarios, and should intuitively be in the same unit as the simulated metric for this purpose. These relationships can be inferred via the presented aligned methods “Perception Sensor Collaborative Effect and Cause Tree” (PerCollECT) and “Cause, Effect, and Phenomenon Relevance Analysis” (CEPRA). For sample validation, each experiment must be accompanied by reference measurements, as these then serve as simulation input. Since the reference data collection is subject to epistemic as well as aleatory uncertainty, which are both propagated through the simulation in the form of input data variation, this leads to several slightly different simulation results. In the simulation of measured signals and data over time considered here, this combination of uncertainties is best expressed as superimposed cumulative distribution functions. The metric must therefore be able to handle such so-called p-boxes as a result of the large set of simulations. In the present work, the area validation metric (AVM) is selected by a detailed analysis as the best of the metrics already used and extended to be able to fulfill all the requirements. This results in the corrected AVM (CAVM), which quantifies the model scattering error with respect to the real scatter. Finally, the double validation metric (DVM) is elaborated as a double-vector of the former metric with the estimate for the model bias. The novel metric is exemplarily applied to the empirical cumulative distribution functions of lidar measurements and the p-boxes from their re-simulations. In this regard, aleatory and epistemic uncertainties are taken into account for the first time and the novel metrics are successfully established. The quantification of the uncertainties and error prediction of a sensor model based on the sample validation is also demonstrated for the first time

    Advancements and Breakthroughs in Ultrasound Imaging

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    Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

    Energy Subtraction Methods as an Alternative to Conventional X-Ray Angiography

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    Digital subtraction angiography (DSA) is a technique that is widely used to enhance the visibility of small vessels obscured by background structures by subtracting a mask and contrast image. However, DSA is generally unsuccessful for imaging the heart due to the motion that occurs between mask and contrasted images which cause motion artifacts. An alternative approach known as energy subtraction angiography (ESA) exploits the iodine k-edge by acquiring contrast images with a low and high kV in rapid succession to bring the benefits of DSA without motion artifacts. However, it was concluded that image quality for ESA could not compete with DSA, and the approach was abandoned. In our work we show that conclusions about iodine SNR for ESA were based on limitations of early technical components that are no longer relevant. The goals of this thesis were to: 1) develop a theoretical model of iodine SNR that is independent of technology for DSA and ESA; 2) optimize the iodine SNR for ESA; 3) image ESA in an anthropomorphic phantom. It is concluded that, when these conditions are satisfied, ESA iodine SNR equal to that of DSA for low iodine mass loadings (artery sizes) for the same patient entrance exposure, and therefore may provide alternatives to DSA in situations where motion artifacts are expected to render a study as non-diagnostic, such as in coronary applications. In the future this will have important applications for subtraction imaging of the coronary arteries and other vessels where stenosis is vital to patient health
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