A new, open-source, multi-modality digital breast phantom

Christian G. Graff

doi:10.1117/12.2216312

22 March 2016 A new, open-source, multi-modality digital breast phantom

Christian G. Graff

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 978309 (2016) https://doi.org/10.1117/12.2216312
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

An anthropomorphic digital breast phantom has been developed with the goal of generating random voxelized breast models that capture the anatomic variability observed in vivo. This is a new phantom and is not based on existing digital breast phantoms or segmentation of patient images. It has been designed at the outset to be modality agnostic (i.e., suitable for use in modeling x-ray based imaging systems, magnetic resonance imaging, and potentially other imaging systems) and open source so that users may freely modify the phantom to suit a particular study. In this work we describe the modeling techniques that have been developed, the capabilities and novel features of this phantom, and study simulated images produced from it. Starting from a base quadric, a series of deformations are performed to create a breast with a particular volume and shape. Initial glandular compartments are generated using a Voronoi technique and a ductal tree structure with terminal duct lobular units is grown from the nipple into each compartment. An additional step involving the creation of fat and glandular lobules using a Perlin noise function is performed to create more realistic glandular/fat tissue interfaces and generate a Cooper’s ligament network. A vascular tree is grown from the chest muscle into the breast tissue. Breast compression is performed using a neo-Hookean elasticity model. We show simulated mammographic and T₁-weighted MRI images and study properties of these images.

Citation Download Citation

Christian G. Graff "A new, open-source, multi-modality digital breast phantom", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978309 (22 March 2016); https://doi.org/10.1117/12.2216312

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