Multistatic radar-based imaging in layered and dispersive media for biomedical applications. (April 2023)

• Record Type: Journal Article
• Authors: Ullah, Rahmat; Saied, Imran; Arslan, Tughrul
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: Microwave imaging -- Alzheimer's disease -- Image reconstruction -- Radar-based algorithm -- Frequency-domain measurements; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104568
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper presents a novel algorithm for near-field radar imaging in layered and dispersive media with application to biomedical imaging. The proposed algorithm compensates for dielectric variations and integrates frequency response to form an image. The algorithm uses both the amplitude and phase of the complex electromagnetic field. The algorithm uses phase shift instead of time delay to overcome the disadvantage of time-shift radar-based algorithms. The algorithm is validated using experimental data for brain tissue affected by plaques/tangles due to Alzheimer's disease. The reconstructed images are compared with the most widely used radar-based algorithms. Abstract: This paper presents a novel algorithm for near-field radar imaging in layered and dispersive media with application to biomedical imaging. The proposed algorithm compensates for variations in the dielectric properties and integrates each component's frequency response to form an image. The algorithm uses both the amplitude and phase of the complex electromagnetic field. The proposed algorithm resolves dispersion issues using frequency-dependent velocity. The algorithm uses phase shift instead of time delay to overcome the disadvantage of time-shift radar-based methods, such as inaccurate signal transformation and wave flight time. Therefore, the proposed algorithm allows detecting small changes in dispersive media such as the human brain. The algorithm is validated using experimental data for different volumes of brain tissue affected by the accumulation of plaques and tangles due to Alzheimer's disease. The accuracy and robustness of the proposed algorithm are evaluated using estimation factor and localisation error.A maximum estimation factor of 1.1 cm and localisation error of 5.3 mm in the actual and detected volume ensure that the proposed algorithm detects small changes in the brain more accurately. The paper also discusses comparisons with radar-based algorithms, demonstrating that the proposed algorithm is less compute-intensive and yields more accurate results.