Toward robust modeling of breast biomechanical compression: an extended study using graph neural networks

Accurate simulation of breast tissue deformation is essential for reliable image registration between 3D imaging modalities and 2D mammograms, where compression significantly alters tissue geometry. Although finite element analysis (FEA) provides high-fidelity modeling, it is computationally intensive and not well suited for rapid simulations. To address this, the physics-based graph neural network (PhysGNN) has been introduced as a computationally efficient approximation model trained on FEA-generated deformations. We extend prior work by evaluating the performance of PhysGNN on new digital breast phantoms and assessing the impact of training on multiple phantoms.

PhysGNN was trained on both single-phantom (per-geometry) and multiphantom (multigeometry) datasets generated from incremental FEA simulations. The digital breast phantoms represent the uncompressed state, serving as input geometries for predicting compressed configurations. A leave-one-deformation-out evaluation strategy was used to assess predictive performance under compression.

Training on new digital phantoms confirmed the model's robust performance, though with some variability in prediction accuracy reflecting the diverse anatomical structures. Multiphantom training further enhanced this robustness and reduced prediction errors.

PhysGNN offers a computationally efficient alternative to FEA for simulating breast compression. The results showed that model performance remains robust when trained per-geometry, and further demonstrated that multigeometry training enhances predictive accuracy and robustness for the geometries included in the training set. This suggests a strong potential path toward developing reliable models for generating compressed breast volumes, which could facilitate image registration and algorithm development.