An Analytically Based Approach for Evaluating the Impact of the Noise on the Microwave Imaging Detection

In a realistic scenario, it is inevitable to have noise on the images due to the noise from the system's hardware, which results in producing inaccurate images. This paper presents an investigation on the impact of adding noises into the simulation for an Ultra-Wideband (UWB) Microwave Imaging (MWI) procedure based on the Huygens principle (HP). A comparison between uniform and Gaussian noises at different amplitudes is provided, with the aim of investigating the detection process for applications such as bone fracture detection. This is done using analytical simulations. To construct the electric field at the perimeter of the external cylinder, simulations have been run mimicking UWB signals transmitted onto a simulated cylindrical bone-mimicking phantom containing an inclusion with different dielectric properties. This field was simulated using MATLAB and generated a value for the electric field at frequencies between 3 and 5 GHz. To investigate the impact of noise on the detection capability, two types of common noises have been applied to the signal at different amplitudes. The resulting images have visually been compared and the imaging performance has also been analysed using an image quantification metric, signal-to-clutter ratio (SCR). The impact of noise on the detection capability was quantified using this image quantification metric

A Phantom Investigation to Quantify Huygens Principle Based Microwave Imaging for Bone Lesion Detection

This paper demonstrates the outcomes of a feasibility study of a microwave imaging procedure based on the Huygens principle for bone lesion detection. This study has been performed using a dedicated phantom and validated through measurements in the frequency range of 1–3 GHz using one receiving and one transmitting antenna in free space. Specifically, a multilayered bone phantom, which is comprised of cortical bone and bone marrow layers, was fabricated. The identification of the lesion’s presence in different bone layers was performed on images that were derived after processing through Huygens’ principle, the S21 signals measured inside an anechoic chamber in multi-bistatic fashion. The quantification of the obtained images was carried out by introducing parameters such as the resolution and signal-to-clutter ratio (SCR). The impact of different frequencies and bandwidths (in the 1–3 GHz range) in lesion detection was investigated. The findings showed that the frequency range of 1.5–2.5 GHz offered the best resolution (1.1 cm) and SCR (2.22 on a linear scale). Subtraction between S21 obtained using two slightly displaced transmitting positions was employed to remove the artefacts; the best artefact removal was obtained when the spatial displacement was approximately of the same magnitude as the dimension of the lesio

An Analytically Based Approach for Evaluating the Impact of the Noise on the Microwave Imaging Detection

In a realistic scenario, it is inevitable to have noise on the images due to the noise from the system’s hardware, which results in producing inaccurate images. This paper presents an investigation on the impact of adding noises into the simulation for an Ultra-Wideband (UWB) Microwave Imaging (MWI) procedure based on the Huygens principle (HP). A comparison between uniform and Gaussian noises at different amplitudes is provided, with the aim of investigating the detection process for applications such as bone fracture detection. This is done using analytical simulations. To construct the electric field at the perimeter of the external cylinder, simulations have been run mimicking UWB signals transmitted onto a simulated cylindrical bone-mimicking phantom containing an inclusion with different dielectric properties. This field was simulated using MATLAB and generated a value for the electric field at frequencies between 3 and 5 GHz. To investigate the impact of noise on the detection capability, two types of common noises have been applied to the signal at different amplitudes. The resulting images have visually been compared and the imaging performance has also been analysed using an image quantification metric, signal-to-clutter ratio (SCR). The impact of noise on the detection capability was quantified using this image quantification metric

Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

Abstract In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0∘, 90∘, 180∘, and 270∘. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An “ideal/reference” image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed

Experimental Validation of Microwave Tomographywith the DBIM-TwIST Algorithm for Brain StrokeDetection and Classification

We present an initial experimental validation of a microwave tomography (MWT) prototype for brain stroke detection and classification using the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm. The validation study consists of first preparing and characterizing gel phantoms which mimic the structure and the dielectric properties of a simplified brain model with a haemorrhagic or ischemic stroke target. Then, we measure the S-parameters of the phantoms in our experimental prototype and process the scattered signals from 0.5 to 2.5 GHz using the DBIM-TwIST algorithm to estimate the dielectric properties of the reconstruction domain. Ourresultsdemonstratethatweareabletodetectthestroketargetinscenarios where the initial guess of the inverse problem is only an approximation of the true experimental phantom. Moreover, the prototype can differentiate between haemorrhagic and ischemic strokes based on the estimation of their dielectric properties

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Ultra-wideband imaging techniques for medical applications

Ultra-wideband (UWB) radio techniques have long promised good contrast and high resolution for imaging human tissue and tumours; however, to date, this promise has not entirely been realised. In recent years, microwave imaging has been recognised as a promising non-ionising and non-invasive alternative screening technology, gaining its applicability to breast cancer by the significant contrast in the dielectric properties at microwave frequencies of normal and malignant tissues. This thesis deals with the development of two novel imaging methods based on UWB microwave signals. First, the mode-matching (MM) Bessel-functions-based algorithm, which enables the identification of the presence and location of significant scatterers inside cylindrically-shaped objects is introduced. Next, with the aim of investigating more general 3D problems, the Huygens principle (HP) based procedure is presented. Using HP to forward propagate the waves removes the need to apply matrix generation/inversion. Moreover, HP method provides better performance when compared to conventional time-domain approaches; specifically, the signal to clutter ratio reaches 8 dB, which matches the best figures that have been published. In addition to their simplicity, the two proposed methodologies permit the capture of a minimum dielectric contrast of 1:2, the extent to which different tissues, or differing conditions of tissues, can be discriminated in the final image. Moreover, UWB allows all the information in the frequency domain to be utilised, by combining information gathered from the individual frequencies to construct a consistent image with a resolution of approximately one quarter of the shortest wavelength in the dielectric medium. The power levels used and the specific absorption rates are well within safety limits, while the bandwidths satisfy the UWB definition of being at least 20% of the centre frequencies. It follows that the methodologies permit the detection and location of significant scatterers inside a volume. Validation of the techniques through both simulations and measurements have been performed and presented, illustrating the effectiveness of the methods.</p