Coronary Angiography

In the intervening 10 years tremendous advances in the field of cardiac computed tomography have occurred. We now can legitimately claim that computed tomography angiography (CTA) of the coronary arteries is available. In the evaluation of patients with suspected coronary artery disease (CAD), many guidelines today consider CTA an alternative to stress testing. The use of CTA in primary prevention patients is more controversial in considering diagnostic test interpretation in populations with a low prevalence to disease. However the nuclear technique most frequently used by cardiologists is myocardial perfusion imaging (MPI). The combination of a nuclear camera with CTA allows for the attainment of coronary anatomic, cardiac function and MPI from one piece of equipment. PET/SPECT cameras can now assess perfusion, function, and metabolism. Assessing cardiac viability is now fairly routine with these enhancements to cardiac imaging. This issue is full of important information that every cardiologist needs to now

Hippocampus dependent and independent theta-networks of working memory maintenance

Working memory is the ability to briefly maintain and manipulate information beyond its transient availability to our senses. This process of short-term stimulus retention has often been proposed to be anatomically distinct from long-term forms of memory. Although it’s been well established that the medial temporal lobe (MTL) is critical to long-term declarative memory, recent evidence has suggested that MTL regions, such as the hippocampus, may also be involved in the working memory maintenance of configural visual relationships. I investigate this possibility in a series of experiments using Magnetoencephalography to record the cortical oscillatory activity within the theta frequency band of patients with bilateral hippocampal sclerosis and normal controls. The results demonstrate that working memory maintenance of configural-relational information is supported by a theta synchronous network coupling frontal, temporal and occipital visual areas, and furthermore that this theta synchrony is critically dependent on the integrity of the hippocampus. Alternate forms of working memory maintenance, that do not require the relational binding of visual configurations, engage dissociable theta synchronous networks functioning independently of the hippocampus. In closing, I will explore the interactions between long-term and short-term forms of memory and demonstrate that through these interactions, memory performance can effectively be improved

Cardiovascular Magnetic Resonance Imaging for the Investigation of Patients with Coronary Heart Disease

Objectives To evaluate the role of stress perfusion cardiovascular magnetic resonance (CMR) in the investigation of stable coronary artery disease (CAD). Background Coronary artery disease remains the biggest cause of morbidity and mortality. The multi-parametric CMR examination is established as an investigative strategy for the investigation of CAD. Methods Study 1 & 2: Patients with stable coronary artery disease underwent a multi-parametric CMR protocol assessing 4 components: i) left ventricular function; ii) myocardial perfusion; iii) viability (late gadolinium enhancement (LGE)) and iv) coronary magnetic resonance angiography (MRA). The diagnostic accuracy of the individual components were assessed. The ischaemic burden of stress CMR Vs. Single Photon Emission Computed Tomography (SPECT) was determined. Study 3: Volunteers and patients were scanned with perfusion sequence which adapts the spatial resolution to the available scanning time and field-of-view. Study 4: A multi-centre pragmatic randomised controlled trial of patients with stable angina comparing CMR guided-care Vs. SPECT guided-care Vs. National Institute of Health and Care Excellence guided-care. Results Study 1 demonstrated the stress perfusion component of the multi-parametric CMR exam was the single most important component for overall diagnostic accuracy. However, the full combined multi-parametric protocol was the optimal approach for disease rule-out, and the LGE component best for rule-in. Study 2 showed that there was reasonable agreement of the summed stress scores between CMR and SPECT (a well established investigation with significant amounts of prognostic data). In study 3, a perfusion pulse sequence which automatically adapts the acquisition sequence to the available scanning time results in spatial resolution improvement and reduction in dark rim artefact. Finally in study 4 in patients with suspected angina using CMR as an initial investigative strategy produced a significantly lower probability of unnecessary angiography compared to NICE guidance. There were similar rates of CAD detection were comparable suggesting no penalty for using functional imaging as a gatekeeper for angiography. Conclusion CMR has high diagnostic accuracy for the detection of coronary artery disease; with similar detection of ischaemic burden to established tests and can be used safely and effectively as a gate keeper to invasive coronary angiography

On the Chirp Function, the Chirplet Transform and the Optimal Communication of Information

—The purpose of this extended paper is to provide a review of the chirp function and the chirplet transform and to investigate the application of chirplet modulation for digital communications, in particular, the transmission of binary strings. The significance of the chirp function in the solution to a range of fundamental problems in physics is revisited to provide a background to the case and to present the context in which the chirp function plays a central role, the material presented being designed to show a variety of problems with solutions and applications that are characterized by a chirp function in a fundamental way. A study is then provided whose aim is to investigate the uniqueness of the chirp function in regard to its use for convolutionalcodinganddecoding,thelattercase(i.e.decoding) being related to the autocorrelation of the chirp function which provides a unique solution to the deconvolution problem. Complementary material in regard to the uniqueness of a chirp is addressed through an investigation into the selfcharacterizationofthechirpfunctionuponFouriertransformation. This includes a short study on the eigenfunctions of the Fourier transform, leading to a uniqueness conjecture which is based on an application of the Bluestein decomposition of a Fourier transform. The conjecture states that the chirp function is the only phase-only function to have a self-characteristic Fourier transform, and, for a specific scaling constant, a conjugate eigenfunction. In the context of this conjecture, we consider the transmission of information through a channel characterized by additive noise and the detection of signals with very low Signal-to-Noise Ratios. It is shown that application of chirplet modulation can provide a simple and optimal solution to the problem of transmitting binary strings through noisy communication channels, a result which suggests that all digital communication systems should ideally by predicated on the application of chirplet modulation. In the latter part of the paper, a method is proposed for securing the communication of information (in the form of a binary string) through chirplet modulation that is based on prime number factorization of the chirplet (angular) bandwidth. Coupled with a quantum computer for factorizing very large prime numbers using Shor’s algorithm, the method has the potential for designing a communications protocol specifically for users with access to quantum computing when the factorization of very large prime numbers is required. In thisrespect,and,inthefinalpartofthepaper,weinvestigatethe application of chirplet modulation for communicating through the ‘Water-Hole’. This includes the introduction of a method for distinguishing between genuine ‘intelligible’ binary strings through the Kullback-Leibler divergence which is shown to be statistically significant for a number of natural languages

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

BOOTSTRAP ENHANCED N-DIMENSIONAL DEFORMATION OF SPACE WITH ACOUSTIC RESONANCE SPECTROSCOPY

Acoustic methods can often be used with limited or no sample preparations making them ideal for rapid process analytical technologies (PATs). This dissertation focuses on the possible use of acoustic resonance spectroscopy as a PAT in the pharmaceutical industry. Current good manufacturing processes (cGMP) need new technologies that have the ability to perform quality assurance testing on all products. ARS is a rapid and non destructive method that has been used to perform qualitative studies but has a major drawback when it comes to quantitative studies. Acoustic methods create highly non linear correlations which usually results in high level computations and chemometrics. Quantification studies including powder contamination levels, hydration amounts and active pharmaceutical ingredient (API) concentrations have been used to test the hypothesis that bootstrap enhanced n-dimensional deformation of space (BENDS) could be used to overcome the highly non linear correlations that occur with acoustic resonance spectroscopy (ARS) eliminating a major drawback with ARS to further promote the device as a possible process analytical technology (PAT) in the pharmaceutical industry. BENDS is an algorithm that has been created to calculate a reduced linear calibration model from highly non linear relationships with ARS spectra. ARS has been shown to correctly identify pharmaceutical tablets and with the incorporation of BENDS, determine the hydration amount of aspirin tablets, D-galactose contamination levels of Dtagatose powders and the D-tagatose concentrations in resveratrol/D-tagatose combinatory tablets

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks