The identification of the unloaded configuration of breast tissue with unknown non-homogenous stiffness parameters using surface measured data in deformed configuration
| Autor: | Mohammad Rahim Hematiyan, M. Hajhashemkhani |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Surface (mathematics) Quantitative Biology::Tissues and Organs Finite Element Analysis Physics::Medical Physics Inverse Breast Neoplasms Health Informatics 03 medical and health sciences 0302 clinical medicine Breast cancer medicine Humans Breast Sensitivity (control systems) skin and connective tissue diseases Mathematics Observational error Phantoms Imaging Mathematical analysis Stiffness Inverse problem medicine.disease Finite element method Computer Science Applications 030104 developmental biology Female medicine.symptom Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Computers in Biology and Medicine. 128:104107 |
| ISSN: | 0010-4825 |
| DOI: | 10.1016/j.compbiomed.2020.104107 |
| Popis: | Large deformation analysis of the breast is known as a useful approach for locating the tumor and treatment strategies of breast cancer, for which knowing the breast stiffness parameters and unloaded configuration is crucial to obtain reliable results. In this study, an iterative inverse finite element algorithm is developed to identify the unloaded configuration of the breast while its stiffness constants are unknown and its internal structure is assumed to be non-homogeneous. The position vector of surface points in the deformed configuration of the breast is employed to obtain the unknowns of the inverse problem. An objective function based on the difference between the position vector of the calculated and measured deformed configurations is defined. Thereafter, the objective function is minimized using a gradient-based method. The sensitivity analysis for material parameters is performed using an analytic direct differentiation approach. Through several numerical examples, the effectiveness of the proposed inverse method for identifying the unloaded configuration of a uniform, a computational breast phantom with a single inclusion as well as a computational breast phantom with randomly distributed stiffness, is demonstrated. The effects of the number of load cases, measurement error, and initial guesses on the results of the inverse problem are investigated, as well. It is observed that the unloaded configuration of the computational breast phantom with a single inclusion or heterogeneous breast tissues can be accurately found by considering an equivalent homogenous material for the tissue. |
| Databáze: | OpenAIRE |
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