A computational framework for patient-specific breast modelling with anatomically informed Cooper's ligament...

Area of Science:

• Biomechanical Engineering
• Medical Imaging
• Computational Modeling

Background:

• Cooper's ligaments are crucial for breast suspension but are inadequately represented in current finite element (FE) models.
• Existing FE models lack patient-specific anatomical detail of Cooper's ligaments, limiting their utility in surgical planning.

Purpose of the Study:

• To develop a patient-specific computational framework that integrates anatomically accurate Cooper's ligament networks into 3D breast FE models.
• To investigate the biomechanical impact of detailed Cooper's ligament architecture on breast tissue behavior.

Main Methods:

• A pipeline combining MRI-based segmentation, deep learning, and FE simulations was used.
• Anatomically informed Cooper's ligament networks were modeled as beam elements within hyperelastic soft tissue domains.
• Three ligament configurations were simulated under gravity to assess their influence on breast displacement and stress distribution.

Main Results:

• Anatomically distributed ligament networks significantly reduced maximum breast displacement compared to absent or simplified models.
• Internal stress was redistributed from the skin to the ligamentous scaffold with a more detailed ligament network.
• Breast biomechanics exhibited a nonlinear stiffness-displacement response, with diminishing returns in stiffness beyond 2 MPa.

Conclusions:

• Breast biomechanics are highly sensitive to Cooper's ligament architecture, necessitating models beyond simplified radial patterns.
• The proposed framework, compatible with clinical MRI and FE tools, enhances patient-specific breast modeling for improved preoperative planning in reconstructive surgery.