Monitoring of intracerebral hemorrhage with a linear microwave imaging algorithm.

Intracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering. Our aim is to reconstruct non-anatomical maps that reveal the volumetric evolution of hemorrhage by using the differences between consecutive electric field measurements. This approach can potentially allow the monitoring of intracerebral hemorrhage in a real-time and cost-effective manner. Here, we devise an indicator function, which reveals the position, volumetric growth, and shrinkage of hemorrhage. Later, the method is numerically tested via a 3D anthropomorphic dielectric head model. Through several simulations performed for different locations of intracerebral hemorrhage, the indicator function-based technique is demonstrated to be capable of detecting the changes accurately. Finally, the robustness under noisy conditions is analyzed to assess the feasibility of the method. This analysis suggests that the method can be used to monitor the evolution of intracerebral hemorrhage in real-world scenarios.

Microwave imaging of breast cancer with factorization method: SPIONs as contrast agent.

Female breast at macroscopic scale is a nonmagnetic medium, which eliminates the possibility of realizing microwave imaging of the breast cancer based on magnetic permeability variations. However, by administering functionalized, superparamagnetic iron oxide nanoparticles (SPIONs) as a contrast material to modulate magnetic permeability of cancer cells, a small variation on the scattered electric field from the breast is achievable under an external, polarizing magnetic field. PURPOSE: We demonstrate an imaging technique that can locate cancerous tumors inside the breast due to electric field variations caused by SPION tracers under different magnetic field intensities. Furthermore, we assess the feasibility of SPION-enhanced microwave imaging for breast cancer with simulations performed on a multi-static imaging configuration. METHODS: The imaging procedure is realized as the factorization method of qualitative inverse scattering theory, which is essentially a shape retrieval algorithm for inaccessible objects. The formulation is heuristically modified to accommodate the scattering parameters instead of the electric field to comply with the requirements of experimental microwave imaging systems. RESULTS: With full-wave electromagnetic simulations performed on an anthropomorphically realistic breast phantom, which is excited with a cylindrical imaging prototype of 18 dipole antenna arranged as a single row, the technique is able to locate cancerous tumors for a experimentally achievable doses. CONCLUSIONS: The technique generates nonanatomic microwave images, which map the cancerous tumors depending on the concentration of SPION tracers, to aid the diagnosis of the breast cancer.

Age group classification and gender detection based on forced expiratory spirometry.

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.