ArticleSensors (Basel, Switzerland)2020
Microwave Bone Imaging: A Preliminary Investigation on Numerical Bone Phantoms for Bone Health Monitoring.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Reducing soft tissue artefacts through projection of markers and microwave imaging: An exploratory study.Scientific reports · 2025Article
- Subspace-Based Two-Step Iterative Shrinkage/Thresholding Algorithm for Microwave Tomography Breast Imaging.Sensors (Basel, Switzerland) · 2025Article
- Repository of MRI-derived models of the breast with single and multiple benign and malignant tumors for microwave imaging research.PloS one · 2024Article
- The bioelectrical properties of bone tissue.Animal models and experimental medicine · 2023Review
- Optimizing Cardiac Wireless Implant Communication: A Feasibility Study on Selecting the Frequency and Matching Medium.Sensors (Basel, Switzerland) · 2023Article
- Dielectric Characterization of Healthy Human Teeth from 0.5 to 18 GHz with an Open-Ended Coaxial Probe.Sensors (Basel, Switzerland) · 2023Article
- An Ex Vivo Study of Wireless Linkage Distance between Implantable LC Resonance Sensor and External Readout Coil.Sensors (Basel, Switzerland) · 2022Article
- A feasibility study on microwave imaging of bone for osteoporosis monitoring.Medical & biological engineering & computing · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Microwave tomography (MWT) can be used as an alternative modality for monitoring human bone health. Studies have found a significant dielectric contrast between healthy and diseased human trabecular bones. A set of diverse bone phantoms were developed based on single-pole Debye parameters of osteoporotic and osteoarthritis human trabecular bones. The bone phantoms were designed as a two-layered circular structure, where the outer layer mimics the dielectric properties of the cortical bone and the inner layer mimics the dielectric properties of the trabecular bone. The electromagnetic (EM) inverse scattering problem was solved using a distorted Born iterative method (DBIM). A compressed sensing-based linear inversion approach referred to as iterative method with adaptive thresholding for compressed sensing (IMATCS) has been employed for solving the underdetermined set of linear equations at each DBIM iteration. To overcome the challenges posed by the ill-posedness of the EM inverse scattering problem, the L2-based regularization approach was adopted in the amalgamation of the IMATCS approach. The simulation results showed that osteoporotic and osteoarthritis bones can be differentiated based on the reconstructed dielectric properties even for low values of the signal-to-noise ratio. These results show that the adopted approach can be used to monitor bone health based on the reconstructed dielectric properties.
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