High Sound-Contrast Inverse Scattering by MR-MF-DBIM Scheme

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Luong Thi Theu (Autor) Outra; Tran Quang-Huy (Autor) Outra; Tran Duc-Nghia (Autor) Outra; Vijender Kumar Solanki (Autor) Outra; Tran Duc-Tan (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Electronics (Switzerland)

Vol. 11 Nº 19

Páginas: 1-3203

ISSN: 2079-9292

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus

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Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências médicas e da saúde

Outras Informações

ID Authenticus: P-00X-BVT

DOI: 10.3390/electronics11193203

Abstract (EN): In ultrasound tomography, cross-sectional images represent the spatial distribution of the physical parameters of a target of interest, which can be obtained based on scattered ultrasound measurements. These measurements can be obtained from dense datasets collected at different transmitter and receiver locations, and using multiple frequencies. The Born approximation method, which provides a simple linear relationship between the objective function and the scattering field, has been adopted to resolve the inverse scattering problem. The distorted Born iterative method (DBIM), which utilizes the first-order Born approximation, is a productive diffraction tomography scheme. In this article, the range of interpolation applications is extended at the multilayer level, taking into account the advantages of integrating this multilayer level with multiple frequencies for the DBIM. Specifically, we consider: (a) a multi-resolution technique, i.e., a multi-step interpolation for the DBIM: MR-DBIM, with the advantage that the normalized absolute error is reduced by 18.67% and 37.21% in comparison with one-step interpolation DBIM and typical DBIM, respectively; (b) the integration of multi-resolution and multi-frequency techniques with the DBIM: MR-MF-DBIM, which is applied to image targets with high sound contrast in a strongly scattering medium. Relative to MR-DBIM, this integration offers a 44.01% reduction in the normalized absolute error.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16