338 research outputs found
Top 2023 Images in Cardiothoracic Imaging
No full textImages in Cardiothoracic Imaging is a manuscript category in Radiology: Cardiothoracic Imaging that aims to showcase compelling and visually appealing images, cutting-edge imaging technologies, and important or rare cardiothoracic imaging diagnoses. Starting this past year, an inaugural team of four trainee deputy editors (Samer Alabed, Gaurav Gulsin, Suvai Gunasekaran, and Domenico Mastrodicasa) have shared the responsibility for reviewing and editing Images in Cardiothoracic Imaging submissions toward publication. Under the leadership of Suhny Abbara, the editor of Radiology: Cardiothoracic Imaging, and Kate Hanneman, an associate editor of the journal and chair of the trainee editorial board, trainee deputy editors maintained the high standard of excellence expected by the readership, ensuring that only the most compelling and relevant manuscripts were featured in Images in Cardiothoracic Imaging.</p
Top 2023 Images in Cardiothoracic Imaging
No full textImages in Cardiothoracic Imaging is a manuscript category in Radiology: Cardiothoracic Imaging that aims to showcase compelling and visually appealing images, cutting-edge imaging technologies, and important or rare cardiothoracic imaging diagnoses. Starting this past year, an inaugural team of four trainee deputy editors (Samer Alabed, Gaurav Gulsin, Suvai Gunasekaran, and Domenico Mastrodicasa) have shared the responsibility for reviewing and editing Images in Cardiothoracic Imaging submissions toward publication. Under the leadership of Suhny Abbara, the editor of Radiology: Cardiothoracic Imaging, and Kate Hanneman, an associate editor of the journal and chair of the trainee editorial board, trainee deputy editors maintained the high standard of excellence expected by the readership, ensuring that only the most compelling and relevant manuscripts were featured in Images in Cardiothoracic Imaging.</p
A computationally efficient detector for MIMO systems
Get PDFIn this work, a newly designed multiple-input multiple-output (MIMO) detector for implementation on software-defined-radio platforms is proposed and its performance and complexity are studied. In particular, we are interested in proposing and evaluating a MIMO detector that provides the optimal trade-off between the decoding complexity and bit error rate (BER) performance as compared to the state of the art detectors. The proposed MIMO decoding technique appears to find the optimal compromise between competing interests encountered in the implementation of advanced MIMO detectors in practical hardware systems where it i) exhibits deterministic decoding complexity, i.e., deterministic latency, ii) enjoys a good complexity–performance trade-off, i.e., it keeps the complexity considerably lower than that of the maximum likelihood detectors with almost optimal performance, iii) allows fully parameterizable performance to complexity trade-off where the performance (or complexity) of the MIMO detector can be adaptively adjusted without the requirement of changing the implementation, iv) enjoys simple implementation and fully supports parallel processing, and v) allows simple and efficient extension to soft-bit output generation for support of turbo decoding. From the simulation results, the proposed MIMO decoding technique shows a substantially improved complexity–performance trade-off as compared to the state of the art techniques
Performance analysis of two-way DF relay selection techniques
No full textAbstractThis work proposes novel bi-directional dual-relay selection techniques based on Alamouti space-time block coding (STBC) using the decode and forward (DF) protocol and analyzes their performance. In the proposed techniques, two- and the three-phase relaying schemes are used to perform bi-directional communication between the communicating terminals via two selected single-antenna relays that employ the Alamouti STBC in a distributed fashion to achieve diversity and orthogonalization of the channels and hence improve the reliability of the system and enable the use of a symbol-wise detector. Furthermore, the network coding strategy applied at all relays is not associated with any power wastage for broadcasting data already known at any terminal, resulting in improved overall performance at the terminals. Our simulations confirm the analytical results and show a substantially improved bit error rate (BER) performance of our proposed techniques compared with the current state of the art
Developing and Implementing a Software Program for Configuring Three Dairy Corral Designs
Full text linkEleven simulation models were developed to plan and design several dairy farm facilities. A decision tree was developed for each simulation model, and then the simulation models were integrated into the relevant decision trees. C# programming language was used to develop a software program via the simulation models and decision trees. The objective is to develop a software program to plan and design dairy farm facilities for dairy farms in hot climates. --------------------------------------------------------------------------------------------------------Assistant Professor, Agricultural Engineering Department, Faculty of Agriculture, Cairo University, Gammaa Street, 12613 Giza, Egypt* Corresponding Author, Email: [email protected] Cite This Article As: Â M. Samer. 2010. Developing and Implementing a Software Program for Configuring Three Dairy Corral Designs. J. Exp. Sci. 1(3): 19-22.Â
Performance analysis of bi-directional relay selection strategy for wireless cooperative communications
No full textAbstract This paper proposes a new two-way double-relay selection strategy for wireless cooperative communication systems with its bit error rate (BER) performance analysis. In this work, two relays are first chosen to maximize the overall system performance in terms of BER. Then, either the two-phase or three-phase protocol is performed to achieve two-directional communications between the communicating terminals through the selected relay nodes that apply orthogonal space-time coding (STC) scheme in a distributed fashion to improve the overall system performance with linear decoding complexity. In other words, the proposed strategy offers an improvement in the reliability of the system and enjoys very low decoding complexity by enabling a symbol-wise decoder. On the other hand, another improvement in the performance at the communication terminals is achieved by performing a network coding method at the selected relay nodes. Furthermore, we offer also analytical approximation of the BER performance for the proposed strategy where the simulation results match perfectly the analytical ones. From the simulation results section, the proposed strategy shows a substantially improved BER performance as compared to the current ones
Computationally Efficient Multi-Antenna Techniques for Multi-User Two-Way Wireless Relay Networks
Get PDFIn this work, we are interested in implementing, developing and evaluating multi-antenna techniques used for multi-user two-way wireless relay networks that provide a good tradeoff between the computational complexity and performance in terms of symbol error rate and achievable data rate. In particular, a variety of newly multi-antenna techniques is proposed and studied. Some techniques based on orthogonal projection enjoy low computational complexity. However, the performance penalty associated with them is high. Other techniques based on maximum likelihood strategy enjoy high performance, however, they suffer from very high computational complexity. The Other techniques based on randomization strategy provide a good trade-off between the computational complexity and performance where they enjoy low computational complexity with almost the same performance as compared to the techniques based on maximum likelihood strategy
Computationally Efficient Spatial and Cooperative Diversity Techniques for Wireless Communication Networks
No full textSeveral techniques are recently proposed to improve the robustness of wireless communication systems, increase the throughput, and overcome channel impairments such as multi-user interference and multi-path fading. Among them, using multiple-antennas is one of the most remarkable techniques as it allows to improve the error performance and the data rate without an increase in the frequency bandwidth or transmitted power. However, multiple-antenna techniques are not applicable in all ad-hoc networks due to hardware constraints. As an alternative, cooperative diversity techniques have been proposed to achieve gains similar to that of multiple-antenna techniques.
In this thesis, we develop computationally efficient multiple-antenna and cooperative diversity techniques for wireless communication networks which offer an improved tradeoff between computational complexity, error performance, and data rate. We first consider space-time block coding for conventional multiple antenna systems. We propose a low complexity decoder for quasi-orthogonal space-time block codes. Both the coherent and non-coherent implementations of this decoder are developed. The proposed decoder can provide a substantially improved tradeoff between the complexity and performance as compared to state-of-the-art decoding techniques. The proposed decoder enjoys a nearly linear decoding complexity and it approximately achieves the optimal performance of the maximum-likelihood decoder.
Recently, cooperative diversity strategies for two-way wireless relay networks have been proposed using the amplify-and-forward and the decode-and-forward
protocols. Although the simultaneous bidirectional decode and-forward transmission has been shown to outperform other decode-and-forward strategies, it has mainly two disadvantages: high relay decoding complexity and the impossibility to use the direct link between the communicating terminals. In this thesis, we propose novel coherent and non-coherent simultaneous bidirectional decode-and-forward distributed space-time coding strategies that provide a higher coding gain and enjoy a substantially lower relay decoding complexity than the state-of-the-art strategies at the same symbol rate. In the proposed strategies, the communicating terminals can benefit from the direct link which is not exploited by other existing simultaneous bidirectional transmission strategies.
Various differential distributed space-time coding strategies for two-way relay networks using the amplify-and-forward protocol which do not require channel
state information either at the relays or at the terminals have been proposed. The simultaneous two-way differential distributed space-time coding strategy
using the amplify-and-forward protocol has been shown to outperform the conventional differential four-phase strategy in the low to medium signal-to-noise ratio region. However, there are mainly three disadvantages associated with it: I) the relay power wasted for transmitting redundant information at either side, ii) the direct link between the communicating terminals can not be used and iii) the considerable bias at high signal-to-noise ratio. In this work, amplify-and-forward differential distributed space-time coding strategies for two-way wireless relay networks are developed, that provide a higher coding gain than the state-of-the art strategies. In the proposed strategies, the relays do not waste power to transmit redundant information at either side and the communicating terminals can fully use the direct link between them.
Although differential distributed space-time coding strategies do not require channel state information at the relays, they are associated with a low error
performance, a high latency, and decoding complexity. Another strategy used in relay networks relies on coherent processing of the relay signals using distributed beamforming techniques. This strategy enjoys a good error performance and low decoding complexity while offering an optimal decoding delay. However, a common requirement in distributed beamforming is the availability of perfect channel state information at all nodes. To avoid this requirement, we introduce a distributed differential beamforming strategy that combines the differential diversity and the distributed beamforming strategy while retaining the benefits of both approaches. The proposed strategy does not require channel state information at any node and enjoys a good error performance, optimal delay, and low decoding complexity
Artificial Intelligence in Cardiac Magnetic Resonance Imaging to Predict Prognosis and Treatment Response
Full text linkBackground
Pulmonary arterial hypertension (PAH) is a serious disease of the heart and lungs. Its impact on patients can be severe, including limitation of day-to-day activities and high mortality. The diagnosis, treatment and monitoring of PAH are challenging and there is a need for tools that can aid clinical decision-making to optimise patient outcomes.
Cardiac MRI (CMR) provides both qualitative and quantitative information about cardiac function and is an important method for evaluating the severity of PAH. The application of machine learning (ML) tools is of growing interest in medical imaging. ML has the potential to automate complex and repetitive tasks, including the rapid segmentation of anatomical structures on images and extraction of clinically useful information.
Aims
This thesis proposes the combination of CMR with two different ML tools to predict prognosis and treatment response in PAH. The first ML tool involves the automated measurement of different cardiac parameters and assesses their utility in predicting prognosis and treatment response. The second ML tool involves the extraction of imaging features directly without the need for segmentation to predict the risk of mortality.
My Contribution
The ML models in this thesis were developed at the University of Sheffield in collaboration with Leiden University. Sheffield is a centre of excellence in PAH treatment thanks to the Sheffield Pulmonary Vascular Disease Unit, which is one of the largest internationally. Each year, more than 700 PAH patients undergo CMR for diagnosis and monitoring. Additionally, each newly diagnosed patient has accompanying in-depth clinical phenotypic data, including right heart catheterisation, exercise and pulmonary function tests, and quality of life assessment. During my research, I created and curated a dataset combining imaging and time-matched clinical data. I identified eligible CMR scans, landmarked and contoured cardiac chambers on multiple sequences and organised the collaboration with computer scientists at Leiden and Sheffield. I arranged image anonymisation, storage and transfer and advised computer scientists on the clinical relevance of CMR images. I performed quality control on ML analyses, collated their results, and analysed the data within clinical context. I have written all chapters in this thesis and clarified the roles of my co-authors at the end of each chapter.
Thesis Outline
Chapter 1 provided an overview of the growing role of CMR in the diagnosis and evaluation of PAH. Chapter 2 summarised the prognostic value of CMR measurements in the prediction of clinical worsening and mortality in PAH patients. Chapter 3 illustrated the rapid expansion of research using AI approaches to automate CMR measurements. The quality of the existing literature was reviewed, significant shortcomings in the transparency of studies were identified and solutions were recommended. Chapter 4 showed our experience in developing, validating and testing a fully automatic CMR segmentation tool. Our tool was developed in one of the largest multi-vendor, multi-centre and multi-pathology reported datasets, and included a large group of patients with right heart disease. We implemented the lessons learned in Chapter 3 and provided extensive descriptions of our datasets, ML model and performance. Our model showed excellent reliability, generalisability, agreement with CMR experts and correlation with invasive haemodynamics. Chapter 5 demonstrated that the automatic CMR measurements allowed assessment of patient-orientated outcomes and prediction of mortality. Thresholds of changes in CMR metrics were identified that could inform clinical decisions in the monitoring of PAH patients. Chapter 6 showed promising results of an ML tool to extrapolate prognostic CMR features with incremental value compared to clinical risk scores and volumetric CMR measurements. Finally, Chapter 7 showed that myocardial T1 mapping could potentially add diagnostic and prognostic value in PAH.
Impact and Future Direction
In addition to the known advantages of ML for providing rapid results with minimal human involvement, the ML tools developed in this thesis allow visualisation of outcomes and are transparent to the human assessor. ML applications to automate the measurement of CMR metrics and extract prognostic imaging features have potential to add clinical value by (i) streamlining prognostication, (ii) informing treatment selection, (iii) assisting the monitoring of treatment response and (iv) ultimately improving clinical decision-making and patient outcomes. Additionally, these tools could point to new CMR end-points for clinical trials, accelerating the development of new treatments for PAH. ML will likely elevate the role of CMR as a powerful prognostic modality in the years to come. Looking ahead, I hope to combine multi-source clinical, imaging and patient-orientated data from several ML tools into a single package to facilitate the assessment of cardiovascular disease
Performance Analysis of Differential Beamforming in Decentralized Networks
Get PDFThis paper proposes and analyzes a novel differential distributed beamforming strategy for decentralized two-way relay networks. In our strategy, the phases of the received signals at all relays are synchronized without requiring channel feedback or training symbols. Bit error rate (BER) expressions of the proposed strategy are provided for coherent and differential M-PSK modulation. Upper bounds, lower bounds, and simple approximations of the BER are also derived. Based on the theoretical and simulated BER performance, the proposed strategy offers a high system performance and low decoding complexity and delay without requiring channel state information at any transmitting or receiving antenna. Furthermore, the simple approximation of the BER upper bound shows that the proposed strategy enjoys the full diversity gain which is equal to the number of transmitting antennas
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