72 research outputs found
The Use of Microwave Tomography in Bone Healing Monitoring
No full textA Master of Science thesis in Biomedical Engineering by Mohanad Ahmed Alkhodari entitled, “The Use of Microwave Tomography in Bone Healing Monitoring”, submitted in April 2019. Thesis advisors are Dr. Amer Zakaria and Dr. Nasser Qaddoumi. Soft and hard copy available.In this thesis, a numerical study is conducted to investigate the use of microwave tomography in monitoring bone health in human lower limbs. By monitoring bone volume fraction (BVF) and bone density, the effectiveness of Vitamin D treatment can be evaluated for osteoporosis patients. In microwave tomography, the leg is radiated with non-ionizing low-power electromagnetic signals with scattered electric fields measured at several locations surrounding the leg. Within the framework of inverse scattering problems, the measured fields are used as inputs for an optimization algorithm to estimate the location and electrical properties inside the human leg. In this work, three two-dimensional cross-sectional models of human leg at different fat thicknesses are created and simulated using a finite-element method, where the transverse magnetic approximation is applied. The synthetic results are then inverted using a finite-element contrast source inversion method. Furthermore, an enhancement procedure is followed to investigate the effect of incorporating prior information about the object-of-interest (OI), changing the boundaries of the imaging domain, relocating antennas, and using ultrasound gel as a matching medium. In addition, an image processing approach is provided to build estimated models to be used in the enhancement procedure. The final results show that variations in BVF affect the results of the inversion algorithm. The real part relative permittivity line plots showed a downward trend as the BVF increases, which can be related to an increase in the bone density. The outcomes of this thesis support the hypothesis that a MWT wearable system is useful for bone density monitoring application, and more specifically for Vitamin D treatment evaluation.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
Normal distribution analysis (mean±std) of the combined mel-frequency cepstral coefficients (MFCCs) using the shallow breathing dataset.
No full textNormal distribution analysis (mean±std) of the combined mel-frequency cepstral coefficients (MFCCs) using the shallow breathing dataset.</p
Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study
No full textA major cause of bone mass loss worldwide is osteoporosis. X-ray is considered to be the gold-standard technique to diagnose this disease. However, there is currently a need for an alternative modality due to the ionizing radiations used in X-rays. In this vein, we conducted a numerical study herein to investigate the feasibility of using microwave tomography (MWT) to detect bone density variations that are correlated to variations in the complex relative permittivity within the reconstructed images. This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). Three anatomically-realistic human leg models based on magnetic resonance imaging reconstructions were created. Each model represents a leg with a distinct fat layer thickness; thus, the three models are for legs with thin, medium, and thick fat layers. In addition to using conventional matching media in the numerical study, the use of commercially available and cheap ultrasound gel was evaluated prior to bone image analysis. The inversion algorithm successfully localized bones in the thin and medium fat scenarios. In addition, bone volume variations were found to be inversely proportional to their relative permittivity in the reconstructed images with the root mean square error as low as 2.54. The observations found in this study suggest MWT as a promising bone imaging modality owing to its safe and non-ionizing radiations used in imaging objects with high quality
Summary table of the current state-of-art works in COVID-19 detection using machine learning and breathing/coughing recordings.
No full textSummary table of the current state-of-art works in COVID-19 detection using machine learning and breathing/coughing recordings.</p
Examples from the deep breathing sounds recorded via smartphone microphone along with their corresponding spectrograms.
No full textShowing: (a-c) COVID-19 subjects (asymptomatic, mild, moderate), (d-f) healthy subjects.</p
Normal distribution analysis (mean±std) of the combined mel-frequency cepstral coefficients (MFCCs) using the deep breathing dataset.
No full textNormal distribution analysis (mean±std) of the combined mel-frequency cepstral coefficients (MFCCs) using the deep breathing dataset.</p
Asymptomatic COVID-19 subjects’ predictions based on the proposed deep learning model.
No full textThe model had a decision boundary of 0.5 to discriminate between COVID-19 and healthy subjects. The values represent a normalized probability regrading the confidence in predicting these subjects as carrying COVID-19.</p
The performance of the deep learning model in predicting COVID-19 and healthy subjects using shallow and deep breathing datasets.
No full textShowing: (a) model’s predictions for both datasets and he corresponding confusion matrices, (b) evaluation metrics including accuracy, sensitivity, specificity, precision, and F1-score, (c) receiver operating characteristic (ROC) curves and corresponding area under the curve (AUROC) for COVID-19 and healthy subjects using both datasets.</p
Examples from the shallow breathing sounds recorded via smartphone microphone along with their corresponding spectrograms.
No full textShowing: (a-c) COVID-19 subjects (asymptomatic, mild, moderate), (d-f) healthy subjects.</p
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