A Gradient-Based Approach for Breast DCE-MRI Analysis

L. Losurdo, T. M.A. Basile, A. Fanizzi, R. Bellotti, U. Bottigli, R. Carbonara, R. Dentamaro, D. Diacono, V. Didonna, A. Lombardi, F. Giotta, C. Guaragnella, A. Mangia, R. Massafra, P. Tamborra, S. Tangaro, D. La Forgia

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

Original languageEnglish
Article number9032408
JournalBioMed Research International
Volume2018
DOIs
Publication statusPublished - Jan 1 2018

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Breast
Magnetic Resonance Imaging
Tissue
Contrast Media
Breast Neoplasms
Breast Diseases
Imaging techniques
Light
Mortality
Neoplasms
Radiologists

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Cite this

Losurdo, L., Basile, T. M. A., Fanizzi, A., Bellotti, R., Bottigli, U., Carbonara, R., ... La Forgia, D. (2018). A Gradient-Based Approach for Breast DCE-MRI Analysis. BioMed Research International, 2018, [9032408]. https://doi.org/10.1155/2018/9032408

A Gradient-Based Approach for Breast DCE-MRI Analysis. / Losurdo, L.; Basile, T. M.A.; Fanizzi, A.; Bellotti, R.; Bottigli, U.; Carbonara, R.; Dentamaro, R.; Diacono, D.; Didonna, V.; Lombardi, A.; Giotta, F.; Guaragnella, C.; Mangia, A.; Massafra, R.; Tamborra, P.; Tangaro, S.; La Forgia, D.

In: BioMed Research International, Vol. 2018, 9032408, 01.01.2018.

Research output: Contribution to journalArticle

Losurdo, L, Basile, TMA, Fanizzi, A, Bellotti, R, Bottigli, U, Carbonara, R, Dentamaro, R, Diacono, D, Didonna, V, Lombardi, A, Giotta, F, Guaragnella, C, Mangia, A, Massafra, R, Tamborra, P, Tangaro, S & La Forgia, D 2018, 'A Gradient-Based Approach for Breast DCE-MRI Analysis', BioMed Research International, vol. 2018, 9032408. https://doi.org/10.1155/2018/9032408
Losurdo L, Basile TMA, Fanizzi A, Bellotti R, Bottigli U, Carbonara R et al. A Gradient-Based Approach for Breast DCE-MRI Analysis. BioMed Research International. 2018 Jan 1;2018. 9032408. https://doi.org/10.1155/2018/9032408
Losurdo, L. ; Basile, T. M.A. ; Fanizzi, A. ; Bellotti, R. ; Bottigli, U. ; Carbonara, R. ; Dentamaro, R. ; Diacono, D. ; Didonna, V. ; Lombardi, A. ; Giotta, F. ; Guaragnella, C. ; Mangia, A. ; Massafra, R. ; Tamborra, P. ; Tangaro, S. ; La Forgia, D. / A Gradient-Based Approach for Breast DCE-MRI Analysis. In: BioMed Research International. 2018 ; Vol. 2018.
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