Background: Scatter correction is crucial to enhance image quality in x-ray imaging. Deep learning (DL) approaches have been proposed as alternatives to Monte Carlo (MC) simulations, offering comparable accuracy with faster processing times. Further research on DL approaches is required to include a wider range of breast shapes, and adapt scatter correction to the novel 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT) modality. Purpose: To develop a 3D generative breast shape model, providing a comprehensive representation of the female population, and enhance DL-based scatter correction for use in 4D DCE-bCT. Methods: 118 patient scans acquired with the bCT system at Radboudumc were segmented into the major breast tissue types (skin, adipose, fibroglandular tissue).