Browsing by Author "Fear, Elise Carolyn"

Now showing 1 - 4 of 4
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    Bone as an Orientable, Smooth Surface: Distance Transforms, Morphometry, and Adaptation
    (2021-08) Besler, Bryce Albert Alphonsus; Boyd, Steven Kyle; Fear, Elise Carolyn; Forkert, Nils Daniel; Manske, Sarah Lynn; Cooper, David Michael Lane; Nielsen, Jorgen S
    Age-related changes in bone fundamentally occur at the surface. Understanding and modeling these changes is the primary means of understanding and preventing age-related fractures. However, this is a challenging task, as the bone microarchitecture changes topology during adaption when rods disconnect and plates form holes. The primary objective is to handle topological changes mathematically and develop computational methods for the simulation of bone adaptation. This thesis develops a model of age-related bone loss based on the axioms that the bone surface is orientable and smooth. First, a novel artifact is discovered and described for the distance transform of sampled signals that limits their applicability in simulation and morphometry. Second, a new transform is defined termed the ``high-order signed distance transform'' that is better than the so-called exact signed distance transform in the sense that it has an order of accuracy greater than one. However, this transform does not permit a unique solution on sampled binary images, and another method is needed. Third, an algorithm is presented for computing the unique, high-order signed distance transform of biphasic materials from computed tomography data. Fourth, a method of performing morphometry on closed surfaces is described that relates existing global bone morphometric techniques to local curvature values. This method works on binary images without the need for signed distance transforms when small changes in the bone volume are permitted. Finally, the morphometry and high-order signed distance transform are integrated into a model of age-related bone loss. Principally, this work establishes bone adaptation as a geometric flow, simulated using level set methods that are efficient and naturally handle topological changes. The contribution of this thesis is the establishment of a strong mathematical foundation for modeling bone adaptation. High accuracy computational methods are defined to integrate the theory into practice. The theory and methods form a rigorous basis for biological theories of bone adaptation and provide techniques for measuring and falsifying theories.
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    Combined Microwave, Ultrasound and Strain Imaging for Breast Tissue Characterization
    (2022-09-14) Talaga, Kasha; Curiel, Laura; Fear, Elise Carolyn; Bento, Mariana Pinheiro
    Today, breast cancer continues to be one of the most common cancers among women. Early detection of breast cancer can provide patients with the best chance of survival. Imaging methods are routinely used to scan for breast abnormalities, which may include X-Ray mammography, MRI, and ultrasound. The standard method is X-Ray mammography, however, using ultrasound in addition to mammography has become common practice. Another form of imaging, elastography, utilizes the same ultrasound equipment, and is useful to differentiate elastic stiffness between tumours and normal tissues. Microwave imaging is an emerging modality that is appealing because it is low-cost and highly safe for patients, as it does not emit harmful radiation. A promising improvement in the field of breast cancer imaging is to utilize the imaging capabilities of these 3 modalities, microwave, ultrasound and strain elastography in order to better locate and identify breast tumours. In this study, we propose the integration of ultrasound and elastography with an existing microwave-based breast imaging system developed at the University of Calgary. This thesis is focused on presenting a platform which includes a prototype for integration and a graphical interface for viewing the images from all modalities. In this work, ultrasound, strain and microwave imaging scans are applied to breast-tissue mimicking phantoms with different inclusion orientations. The images are investigated to compare image results in various scenarios. Secondly, to integrate the ultrasound and microwave devices, a prototype was developed to allow the ultrasound probe and microwave antennas to have contact with the phantom. The prototype is validated by acquiring both ultrasound and microwave images. Lastly, a graphical interface where images from each modality are viewed is introduced. Here, strain and microwave images are overlayed to provide further information on the inclusion.
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    Pipeline Defect Detection and Localization Using Artificial Intelligence-Based Active Acoustic Sensing
    (2024-04-26) Gulam Dhasthagir, Yaser Arafath; Hugo, Ron; Park, Simon; Sudak, Leszek Jozef; Fear, Elise Carolyn
    In order to maintain the integrity and safety of pipeline systems, structural health monitoring (SHM) is essential for continuous assessment. This study introduces a novel artificial intelligence (AI)-augmented active acoustic sensing technique for uninterrupted pipeline defect detection and localization. Conventional inspection methods, such as in-line inspection (ILI), are hindered by their accessibility, infrequent application, time-intensive data analysis, and the necessity for defect-specific tools. These conventional methods typically rely on intricate transducer/receiver configurations and traditional wave processing techniques, necessitating exact knowledge of pipeline geometry. In this study, we utilize judicious combinations of numerical and experimental tests to detect defects. The method aims to reduce the effects of wave dispersion and streamline the detection process. Finite element analysis (FEA) is a numerical technique used to investigate wave propagation dynamics within the pipe wall. Experimentally, an axial piezo transducer is used to introduce acoustic energy into the pipe wall, while multiple acoustic emission sensors are placed throughout the pipe test section that serve as distributed receivers. To simulate damage, a circumferential notch-type defect is created on the external surface of the pipe. The initial acoustic stimulation employs sinusoidal wave packets of 50 kHz and 70 kHz. However, these commonly used frequencies in defect assessment are known to attenuate quickly, posing challenges in accurately pinpointing defects. To address this, the natural frequencies of the system are identified, and two frequencies close to these resonant frequencies chosen as excitation sources. This innovative approach demonstrates effectiveness in both defect detection and localization tasks, showcasing its potential as a more reliable method. Acknowledging the velocity of longitudinal modes within the selected frequency range, we determine an optimal signal duration to counteract end pipe reflections. Each response collected from the pipe is subject to power spectral analysis, with the root mean square error (RMSE) computed against baseline references. The AI component of this method, anchored by a neural network model, not only enhances performance and adaptability but also, as shown in our study, achieves higher accuracies in both defect detection and localization tasks when contrasting conventional frequencies with those near the resonant frequencies of the system. The model is trained using diverse data, including signals from various locations and those with added noise. To manage variations in signal-to-noise ratio and wave propagation attenuation, Z-score normalization is applied to preprocess the RMSE data. Additionally, the study investigates the application of a highly sensitive Lock-in Amplifier, from which directly generated features, along with indirectly computed redundant features, introduce a novel approach in feature-based defect detection and localization tasks. These features are integrated into the AI component, enhancing the sensitivity of signal measurement. By achieving reasonable accuracy in detection and localization, this method proves crucial in scenarios where signals are completely obscured by noise, necessitating a more extended experimental setup. Thus, this AI-powered active acoustic sensing technique effectively differentiates between intact and damaged pipes and locates the defect within the range of two sensors, demonstrating its viability for practical use in pipeline integrity management. Furthermore, phase response analysis successfully pinpoints the exact location of the defect with reasonable accuracy, despite the presence of multiple reflections.
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    Tomographic Approach to Human Hydration Assessment: Proof of Concept
    (2021-09) Besler, Brendon Chisholm Brian; Fear, Elise Carolyn; Okoniewski, Michal; Lamoureux, Michael Phillip
    Hydration is important to human health as water is a vital nutrient used in many physiological functions within the body. While there are many conventional hydration assessment techniques, there is no non-invasive clinical gold standard. This thesis provides a proof of concept for applying standard microwave tomography techniques to assess clinically relevant changes in hydration. A practical and effective tomographic system is designed in simulation for hydration assessment. To improve blind inversions of high contrast objects, a novel regularization parameter selection procedure is introduced. The tomographic hydration assessment technique is validated in silico using realistic forearm models and the effect of measurement noise and positioning is analyzed. Microwave hydration assessment is evaluated in a group of fasting volunteers. This thesis provides promising results for an emerging technique of hydration assessment based on the estimation of changes in permittivity and a practical application of microwave tomography.