Using prior information to enhance microwave tomography images in bone health assessment.
Mohanad AlkhodariAmer ZakariaNasser QaddoumiPublished in: Biomedical engineering online (2022)
An anatomically realistic finite-element (FE) model representing the human leg was initially generated and filled with corresponding tissues' (skin, fat, muscles, and bones) dielectric properties. Then, numerically, the forward and inverse MWT problems were solved within the framework of the finite-element method-contrast source inversion algorithm (FEM-CSI). Furthermore, image reconstruction enhancements were investigated by utilizing prior information about different tissues as an inhomogeneous background as well as by adjusting the imaging domain and antennas locations based on the prior structural information. In addition, the utilization of a medically approved matching medium that can be used in wearable applications, namely an ultrasound gel, was suggested. Additionally, an approach based on k-means clustering was developed to extract the prior structural information from blind reconstructions. Finally, the enhanced images were used to monitor variations in BVF.
Keyphrases
- finite element
- deep learning
- health information
- mental health
- gene expression
- healthcare
- convolutional neural network
- endothelial cells
- public health
- magnetic resonance
- magnetic resonance imaging
- high resolution
- optical coherence tomography
- adipose tissue
- soft tissue
- oxidative stress
- contrast enhanced
- bone mineral density
- risk assessment
- atomic force microscopy
- image quality
- rna seq
- human health