ArticleLife (Basel, Switzerland)2021
Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change.
Article in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Patient-specific visco-hyperelastic mechanical model for breast tumor localization in surgical planning.Bioengineering & translational medicine · 2026Article
- A quantitative method to compare regional tumor contrast between prone and supine breast MRI.Frontiers in oncology · 2026Article
- FEM simulation of breast deformation with semi-fluid representation.International journal of computer assisted radiology and surgery · 2025Article
- Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling.Clinical biomechanics (Bristol, Avon) · 2023Article
- Predictors of glucocorticoid treatment intensity after endoscopic transsphenoidal surgery in patients with non-functional pituitary adenomas: a retrospective study.Frontiers in neurologyArticle
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe strategy to combat the problem associated with large deformations in the breast due to the difference in the medical imaging of patient posture plays a vital role in multimodal medical image registration with artificial intelligence (AI) initiatives. How to build a breast biomechanical model simulating the large-scale deformation of soft tissue remains a challenge but is highly desirable.
methodsThis study proposed a hybrid individual-specific registration model of the breast combining finite element analysis, property optimization, and affine transformation to register breast images. During the registration process, the mechanical properties of the breast tissues were individually assigned using an optimization process, which allowed the model to become patient specific. Evaluation and results: The proposed method has been extensively tested on two datasets collected from two independent institutions, one from America and another from Hong Kong.
conclusionsOur method can accurately predict the deformation of breasts from the supine to prone position for both the Hong Kong and American samples, with a small target registration error of lesions.
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