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Conventional radar-based image reconstruction techniques fail when they are applied to heterogeneous breast tissue, since the underlying in-breast relative permittivity is unknown or assumed to be constant. This results in a systematic error during the process of image formation. A recent trend in microwave biomedical imaging is to extract the relative permittivity from the object under test to improve the image reconstruction quality and thereby to enhance the diagnostic assessment. In this paper, we present a novel radar-based methodology for microwave breast cancer detection in heterogeneous breast tissue integrating a 3D map of relative permittivity as a priori information. This leads to a novel image reconstruction formulation where the delay-and-sum focusing takes place in time rather than range domain. Results are shown for a heterogeneous dense (class-4) and a scattered fibroglandular (class-2) numerical breast phantom using Bristol's 31-element array configuration.
Sparse sensor networks for Lamb wave-based structural health monitoring (SHM) can detect defects in plate-like structures. However, the limited number of sensor positions provides little information to characterize the unknown scatterer. This can be achieved by full wavefield analysis e.g. using Laser Doppler vibrometry measurements.
This paper proposes deconvolution processing that enhances the acoustic wavefield interpretation by increasing the temporal resolution of the underlying ultrasound signals. Applying this preprocessor to the whole wavefield allows improved non-destructive assessment of the defect. This approach is verified experimentally through a case study on an isotropic aluminum plate with four cracks.