Generative compressed breast shape model for digital mammography and digital breast tomosynthesis
Authors: Pinto Marta C. , Michielsen Koen , Biniazan Ramyar , Kappler Steffen , Sechopoulos Ioannis 
Published: November 2022

Background

Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast‐like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms.

Purpose

To develop a point distribution model of the breast shape obtained through principal component analysis (PCA) of structured light (SL) scans from patient compressed breasts.

Methods

SL scans were acquired at our institution during routine craniocaudal‐view DBT imaging of 236 patients, creating a dataset containing DBT and SL scans with matching information. Thereafter, the SL scans were cleaned, merged, simplified, and set to a regular grid across all cases. A comparison between the initial SL scans after cleaning and the gridded SL scans was performed to determine the absolute difference between them. The scans with points in a regular grid were then used for PCA. Additionally, the correspondence between SL scans and DBT scans was assessed by comparing features such as the chest‐to‐nipple distance (CND), the projected breast area (PBA) and the length along the chest‐wall (LCW). These features were compared using a paired t‐test or the Wilcoxon signed rank sum test. Thereafter, the PCA shape prediction and SL scans were evaluated by calculating the mean absolute error to determine whether the model had adequately captured the information in the dataset. The coefficients obtained from the PCA could then parameterize a given breast shape as an offset from the sample means. We also explored correlations of the PCA breast shape model parameters with certain patient characteristics: age, glandular volume, glandular density by mass, total breast volume, compressed breast thickness, compression force, nipple location, and centre of the chest‐wall.

Results

The median value across cases for the 90th and 99th percentiles of the interpolation error between the initial SL scans after cleaning and the gridded SL scans was 0.50 and 1.16 mm, respectively. The comparison between SL and DBT scans resulted in small, but statistically significant, mean differences of 1.6 mm, 1.6 mm, and 2.2 cm2 for the LCW, CND, and PBA, respectively. The final model achieved a median mean absolute error of 0.68 mm compared to the scanned breast shapes and a perfect correlation between the first PCA coefficient and the patient breast compressed thickness, making it possible to use it to generate new model‐based breast shapes with a specific breast thickness.

Conclusion

There is a good agreement between the breast shape coverage obtained with SL scans used to construct our model and the DBT projection images, and we could therefore create a generative model based on this data that is available for download on Github.