686 research outputs found

    Marshall Space Flight Center Research and Technology Report 2019

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    Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come

    On direct and inverse problems related to longitudinal impact of non-uniform elastic rods

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    The impact of two non-uniform elastic rods is considered and a recursive method was developed that solves the inverse problem: Find the location-dependent impedance function of the impacting rod that generates a prescribed impact force. The developed method delivers exact solutions in closed-form. Moreover, a condition is derived which states if a physically meaningful solution exists. Finally, the underlying 1D model has been validated experimentally

    X-ray absorption tomography employing a conical shell beam

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    We demonstrate depth-resolved absorption imaging by scanning an object through a conical shell of X-rays. We measure ring shaped projections and apply tomosynthesis to extract optical sections at different axial focal plane positions. Three-dimensional objects have been imaged to validate our theoretical treatment. The novel principle of our method is scalable with respect to both scan size and X-ray energy. A driver for this work is to complement previously reported methods concerning the measurement of diffracted X-rays for structural analysis. The prospect of employing conical shell beams to combine both absorption and diffraction modalities would provide enhanced analytical utility and has many potential applications in security screening, process control and diagnostic imaging

    Echocardiography

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    The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

    Role of deep learning techniques in non-invasive diagnosis of human diseases.

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    Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis

    Cost-effective aperture arrays for SKA Phase 1: single or dual-band?

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    An important design decision for the first phase of the Square Kilometre Array is whether the low frequency component (SKA1-low) should be implemented as a single or dual-band aperture array; that is, using one or two antenna element designs to observe the 70-450 MHz frequency band. This memo uses an elementary parametric analysis to make a quantitative, first-order cost comparison of representative implementations of a single and dual-band system, chosen for comparable performance characteristics. A direct comparison of the SKA1-low station costs reveals that those costs are similar, although the uncertainties are high. The cost impact on the broader telescope system varies: the deployment and site preparation costs are higher for the dual-band array, but the digital signal processing costs are higher for the single-band array. This parametric analysis also shows that a first stage of analogue tile beamforming, as opposed to only station-level, all-digital beamforming, has the potential to significantly reduce the cost of the SKA1-low stations. However, tile beamforming can limit flexibility and performance, principally in terms of reducing accessible field of view. We examine the cost impacts in the context of scientific performance, for which the spacing and intra-station layout of the antenna elements are important derived parameters. We discuss the implications of the many possible intra-station signal transport and processing architectures and consider areas where future work could improve the accuracy of SKA1-low costing.Comment: 64 pages, 23 figures, submitted to the SKA Memo serie

    Motion tracking tMRI datasets to quantify abnormal left ventricle motion using finite element modelling

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    According to `The Atlas of Heart Disease and Stroke'[MMMG04] published by the World Health Organization, heart disease accounts for nearly half the deaths in both the developed and developing countries and is the world's single biggest killer. However, early detection of a diseased heart condition can prevent many of these fatalities. Regional wall motion abnormalities of the heart precede both ECG abnormalities and chest pain as an indicator of myocardial ischaemia and are an excellent indicator of coronary stenosis [GZM97]. These motion abnormalities of the heart muscle are difficult to observe and track, because the heart is a relatively smooth organ with few landmarks and non-rigid motion with a twisting motion or tangential component. The MRI tissue-tagging technique gives researchers the first glimpse into how the heart actually beats. This research uses the tagged MRI images of the heart to create a three dimensional model of a beating heart indicating the stress of a region. Tagged MRI techniques are still developing and vary vastly, meaning that there needs to be a methodology that can adapt to these changes rapidly and effectively, to meet the needs of the evolving technology. The focus of this research is to develop and test such a methodology by the means of a Strain Estimation Pipeline along with an effective way of validating any changes made to the individual processes that it comprises of
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