An Investigation of Radiometer and Antenna Properties for Microwave Thermography

Microwave thermography obtains information about the subcutaneous body temperature by a spectral measurement of the intensity of the natural thermally generated radiation emitted by the body tissues. At lower microwave frequencies the thermal radiation can penetrate through biological tissue for significant distances. The microwave thermal radiation from inside the body can be detected and measured non-invasively at the skin surface by the microwave thermography technique, which uses a radiometer to measure the radiation which is received from an antenna on the skin. In the microwave region the radiative power received from a volume of material has a dependence on viewed tissue temperature T(r) of the form, where k is the Boltzmann's constant, B the measurement bandwidth, c(r) is the relative contribution from a volume element dv (the antenna weighting function). The weighting function, c(r), depends on the structure and the dielectric properties of the tissue being viewed, the measurement frequency and the characteristics of the antenna. In any practical radiometer system the body microwave thermal signal has to be measured along with a similar noise signal generated in the radiometer circuits. The work described in this thesis is intended to lead to improvement in the performance of microwave thermography equipment through investigations of antenna weighting functions and radiometer circuit noise sources. All work has been carried out at 3.2 GHz, the central operating frequency of the existing Glasgow developed microwave thermography system. The effects of input circuit losses on the operation of the form of Dicke radiometer used for the Glasgow equipment have been investigated using a computational model and compared with measurements made on test circuits. Very good agreement has been obtained for modelled and measured behaviour. The losses contributed by the microstrip circuit structure, that must be used in the radiometer at 3.2 GHz, have been investigated in detail. Microwave correlation radiometry, by "add and square" method, has been applied to the received signals from a crossed-pair antenna arrangement, the antennas being arranged to view a common region at a certain depth. The antenna response has been investigated using a noise source and by the nonresonant perturbation technique. The received pattern formed by the product of the individual antenna patterns gives a maximum depth in phantom dielectric material. The depth can be adjusted by changing the spacing of the antennas and the phase in an antenna path. However, the pattern is modulated by a set of positive and negative interference fringes so that the complete receive pattern has a complicated form. On uniform temperature distributions the total radiometric signal is zero with the positive and negative contributions cancelling each other out. The fringe modulation can be removed by placing the antennas close enough together, The pattern is then simple and gives a modest maximum response at a known depth in a known material. The radiometer system remains sensitive to the temperature gradients only and the wide range of dielectric properties and tissue structures in the region being investigated usually makes the system response difficult to interpret. For crossed-pair antennas in phase the effective penetration depth in high-and medium-water content tissues is about 2.5 cm at a frequency of 3.2 GHz. The field pattern observed was of the form expected from the measurements of the individual antenna behaviour with the appropriate interference pattern superimposed. The nonresonant perturbation technique has been developed and applied to assist the development of the medical application of both microwave thermographic temperature measurement and microwave hyperthermia induction. These techniques require the electromagnetic field patterns of the special antennas used to be known. These antennas are often formed by short lengths of rectangular or cylindrical waveguide loaded with a low-loss dielectric material to achieve good coupling to body tissues. The high microwave attenuation in biological materials requires the field configurations to be measured close to the antenna aperture in the near-field wave. The nonresonant perturbation is a simple technique which can be used to measure electromagnetic fields in lossy material close to the antenna. It has been applied here to measure accurately the antenna weighting function and the effective penetration depth in tissue simulating dielectric phantom materials. (Abstract shortened by ProQuest.)

Classification automatique de la densité des tissus mammaires

Le cancer du sein est un problème de santé publique. L’imagerie médicale est l’un des éléments clés dans le diagnostic. Cependant, la qualité d’interprétation d’une mammographie reste variable. Une des caractéristiques de l’anatomie et de la physiologie du sein est la densité du tissu mammaire qui est importante pour deux raisons principales : (1) la densité mammaire accrue est associée à une diminution de sensibilité de la mammographie pour la détection du cancer du sein (Schetter, 2014), (2) la densité du sein est l’un des plus importants facteurs de risque connus pour le cancer du sein (Prevrhal et al., 2002 ; Boyd et al., 1995). Le classement automatique de la densité des tissus est donc un processus important dans le diagnostic. De plus, le système de classification BI-RADS identifie quatre niveaux de densité du sein, mais la base de données mini-MIAS est divisée en trois catégories de densité. Dans cet article, nous décrivons une méthode pour la classification de la densité globale du sein en utilisant les réseaux de neurones artificiels. Cette approche présente l’avantage de ne pas nécessiter d’étape de prétraitement et de s’adapter aux différentes bases de données de mammographies. La validité de notre méthode est démontrée en utilisant 240 mammographies de la base de données DDSM et 180 mammographies de la base de données mini-MIAS, avec un taux de classification correcte de 87,50 % et 86,11 %, respectivement.Breast cancer is an international public health concern. Medical imaging is one of the key elements in diagnosis. However, the quality of the interpretation of mammograms remains variable. One of the important characteristics in breast anatomy and physiology is breast tissue density. Density is important for two main reasons: first, increased breast density is associated with decreased mammographic sensitivity for the detection of breast cancer (Schetter, 2014). Second, breast density is one of the strongest known risk factors for breast cancer (Prevrhal et al., 2002; Boyd et al., 1995). For these reasons, automatic tissue density classification is an important process in diagnosis. Moreover, the BI-RADS (Breast Imaging-Reporting And Data System) classification system identifies four levels of breast density, but the mini-MIAS (Mammographic Image Analysis Society) database is divided into three density categories. In this article we describe a method for overall breast density classification using artificial neural networks. This approach has the advantages of not requiring a preprocessing step and the ability to be adapted to different mammography databases. The validation of our method is demonstrated using 240 mammograms from the DDSM database and 180 mammograms from mini-MIAS database, with the correct classification rate of 87.50% and 86.11%, respectively

DECOMPOSITION D’UNE SEQUENCE D’IMAGES DYNAMIQUES DU COEUR EN COMPOSANTES SANGUINE ET TISSULAIRE, EN TOMOGRAPHIE D'EMISSION PAR POSITRONS, PAR LA METHODE DE REDUCTION LINEAIRE DE DIMENSION.

La Méthode de la Réduction Linéaire des Dimensions (Linear Dimension Reduction, LDR) repose sur le principe de la classification par projection entre espaces vectoriels. C'est une technique alternative pour surmonter les limites et les insuffisances de l’analyse factorielle et la méthode des régions d’intérêt, des méthodes utilisées souvent dans le traitement automatique des séquences d’images médicales en vue d’extraire le plus efficacement possible, les paramètres cliniques nécessaires au diagnostic. Dans cet article, nous développons l’aspect théorique fondamental de la méthode suivi de sa démarche algorithmique. L'application de la technique est effectuée par la suite dans la décomposition d’une série d’images dynamiques du cœur du rat acquise en tomographie d'émission par positrons (TEP), en composantes sanguine et tissulaire avec un bruit optimal. La décomposition des images tomographiques avec LDR permet la localisation des tissus dans les images et d'en augmenter le contraste contribuant ainsi à une simplification des procédures des analyses quantitatives en TEP

Applying Cognitive Interviewing to Inform Measurement of Partnership Readiness: A New Approach to Strengthening Community–Academic Research

Partnerships between academic and community-based organizations can richly inform the research process and speed translation of findings. While immense potential exists to co-conduct research, a better understanding of how to create and sustain equitable relationships between entities with different organizational goals, structures, resources, and expectations is needed

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK.

BACKGROUND: A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. METHODS: This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. FINDINGS: Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0-75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4-97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8-80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3-4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. INTERPRETATION: ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials. FUNDING: UK Research and Innovation, National Institutes for Health Research (NIHR), Coalition for Epidemic Preparedness Innovations, Bill & Melinda Gates Foundation, Lemann Foundation, Rede D'Or, Brava and Telles Foundation, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and AstraZeneca

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK

Background A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. Methods This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. Findings Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0–75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4–97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8–80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3–4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. Interpretation ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials

Anisotropy analysis of textures using wavelets transform and fractal dimension

International audienceIn this paper, we propose a new method based on texture analysis of anisotropy. Our proposed method based on a combination of fractal analysis with preprocessing using histogram equalization with discrete wavelets transforms (DWT). First, the texture image enhanced using the histogram equalization. Then, the enhanced image rotated with differences angles from 0° to 360° with a step of 15°. After that, we applied the DWT; we have chosen the Daubechies Wavelets (dbn) for each rotated images. This step followed by the fractal analysis using the differential box counting (DBCM) to the approximate image to estimate de directional fractal dimension (FD). Finally, the degree of anisotropy (DA) calculated as the ratio between the maximum and the minimum value of the FD. The originality of our work reside in the use of the Daubechies Wavelets (dbn) in particular the use of approximate image with the fractal analysis by estimating the directional FD and analysis of the anisotropy. The testing and evaluation of our algorithm are carried out using some textures of the Lille INSERM database U 703 that contains two modalities (MRI and CT-Scan) of bone trabecular texture ROI (Region Of Interest) healthy and pathologic and some Brodatz texture

Staistical features extraction in wavelet domain for texture classification

ISBN 978-1-7281-3157-3 e-SIBN 978-1-7281-3156-6International audienceThis paper presents a new approach for texture classification generalizing a well-known statistical features combining the fractal analysis by means of fractal dimension (FD) with the selection first and second order statistics features in the spatial and wavelet domain. The objective of our paper is to propose the features extraction using statistical parameters in the spatial domain and in wavelet domain with different wavelets, with and without preprocessing stage for the texture classification using neural networks for pattern recognition and studying the effect of the preprocessing and wavelets in classification accuracy. The extracted features are used as the input of the ANN classifier. The performance of the proposed methods are evaluated by using two classes of Brodatz database textures. Finally, classification assessment measures such as the confusion matrix, ROC curves and accuracy are applied to the proposed methods