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

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

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy

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

Ultra-Wideband (UWB) Systems in Biomedical Sensing

The extremely low power transmission levels of ultra-wideband (UWB) technology, alongside its advantageously large bandwidth, make it a prime candidate for being used in numerous healthcare scenarios, which require short-range high-data-rate communications and safe radar-based applications [...

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.EThOS - Electronic Theses Online ServiceEPSRCGBUnited Kingdo

Analytically-based approach for the analysis of MRI volume coil loaded with multilayered cylinder

An analytical approach for the analysis of volume Radio Frequency (RF) coils in use of Magnetic Resonance Imaging (MRI) is presented here. The approach is based on a 2-D full wave solutions and permits to address the problem of volume resonators loaded with multilayered concentric and eccentric cylinders. Human-type tissues can be assumed in the multilayered cylinder model