2016
DOI: 10.1007/978-3-319-47154-9_12
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Trends in Biomechanical Finite Element Breast Deformation Modelling

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Cited by 1 publication
(2 citation statements)
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“…Each medical image is acquired under different conditions, including the position of the patient which can be prone (e.g., MR images) or supine (e.g., computed tomography (CT) images). Tracking features within the breast between these two positions or in a standing position, under the effect of gravity, is highly important for clinical applications ( Babarenda Gamage et al, 2017 ; Danch-Wierzchowska et al, 2017 , 2018 ), such as postural deformation analysis ( Na et al, 2019 ), surgical planning ( Samani et al, 2003 ; Na et al, 2019 ) or implant performance analysis ( Dong et al, 2016 ; Myung et al, 2019 ; Na et al, 2019 ).…”
Section: Finite Element Analysismentioning
confidence: 99%
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“…Each medical image is acquired under different conditions, including the position of the patient which can be prone (e.g., MR images) or supine (e.g., computed tomography (CT) images). Tracking features within the breast between these two positions or in a standing position, under the effect of gravity, is highly important for clinical applications ( Babarenda Gamage et al, 2017 ; Danch-Wierzchowska et al, 2017 , 2018 ), such as postural deformation analysis ( Na et al, 2019 ), surgical planning ( Samani et al, 2003 ; Na et al, 2019 ) or implant performance analysis ( Dong et al, 2016 ; Myung et al, 2019 ; Na et al, 2019 ).…”
Section: Finite Element Analysismentioning
confidence: 99%
“…Besides studying the influence of gravity, mainly between patients’ positions, also the impact of compression is highly important. The mechanical properties of the breast are, as mentioned before, a key element to diagnose pathologies, which leads to diagnostic techniques, such as mammography or elastography, based on compression ( Danch-Wierzchowska et al, 2017 ). Therefore, modeling the breast under compression, mimicking what happens in a mammography exam, was the topic of research for Sturgeon et al (2016) and Liu et al (2017) .…”
Section: Finite Element Analysismentioning
confidence: 99%