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Comparing Radar-Based Breast Imaging Algorithm Performance with Realistic Patient-Specific Permittivity Estimation.
O'Loughlin, Declan; Oliveira, Bárbara L; Glavin, Martin; Jones, Edward; O'Halloran, Martin.
Afiliação
  • O'Loughlin D; Electrical and Electronic Engineering, National University of Ireland Galway, Galway H91CF50, Ireland.
  • Oliveira BL; Electrical and Electronic Engineering, National University of Ireland Galway, Galway H91CF50, Ireland.
  • Glavin M; Electrical and Electronic Engineering, National University of Ireland Galway, Galway H91CF50, Ireland.
  • Jones E; Electrical and Electronic Engineering, National University of Ireland Galway, Galway H91CF50, Ireland.
  • O'Halloran M; Electrical and Electronic Engineering, National University of Ireland Galway, Galway H91CF50, Ireland.
J Imaging ; 5(11)2019 Nov 19.
Article em En | MEDLINE | ID: mdl-34460510
Radar-based breast imaging has shown promise as an imaging modality for early-stage cancer detection, and clinical investigations of two commercial imaging systems are ongoing. Many imaging algorithms have been proposed, which seek to improve the quality of the reconstructed microwave image to enhance the potential clinical decision. However, in many cases, the radar-based imaging algorithms have only been tested in limited numerical or experimental test cases or with simplifying assumptions such as using one estimate of permittivity for all patient test cases. In this work, the potential impact of patient-specific permittivity estimation on algorithm comparison is highlighted using representative experimental breast phantoms. In particular, the case studies presented help show that the permittivity estimate can impact the conclusions of the algorithm comparison. Overall, this work suggests that it is important that imaging algorithm comparisons use realistic test cases with and without breast abnormalities and with reconstruction permittivity estimation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Imaging Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Imaging Ano de publicação: 2019 Tipo de documento: Article