Poster Poster Program Diagnostic and Interventional Radiology Physics

B-Trac – Breast Tissue Rotation and Compression Apparatus for Calibration

Abstract
Purpose

Mammography (compressed 2D) and MRI (uncompressed 3D) capture breast tissue under different conditions, complicating tumor localization across modalities. To bridge this gap, we developed a customizable physical platform to simulate clinical breast compression and generate 3D deformation data for training a mathematical coordinate transformation model.

Methods

The platform was constructed with two parallel plates: a fixed transparent front plate and a movable matte-black rear plate. The black plate features an embedded deformation grid to enable visual tracking of surface displacement, and compression is applied through calibrated force increments to replicate the clinical range. The system features a rotatable phantom mounting mechanism, implemented through a twist-lock enclosure. The male component is embedded into the posterior of the phantom, while the female component is fixed to a vertical support plate orthogonal to the compression plates. This allows the phantom to be rotated (0°, 45°, and 90°); mirroring clinical views and enabling 3D reconstruction. The phantom is cast in Ecoflex™ silicone, selected for its tissue-mimicking elasticity while permitting internal visualization. Colored seed beads are embedded as internal tracers for optical tracking during compression experiments using multi-angle video capture. The system accommodates changes in force, density, and geometry to reflect patient variability using mammography calibration data.

Results

The platform successfully simulates clinical compression views, enables phantom rotation for 3D multi-angle analysis, and allows precise internal tracer tracking. The device provides a reliable experimental foundation for generating deformation data necessary to train predictive models aligned with clinical imaging workflows

Conclusion

We developed a customizable, mechanically validated platform for simulating breast compression under clinically relevant conditions. This work lays the groundwork for creating a mathematical model to predict tissue displacement and enable accurate tumor coordinate mapping between mammography and MRI, with the ultimate goal of improving diagnostic accuracy and surgical planning in breast cancer care.

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