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

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.

[1]  R. Siddon Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[2]  William J. Schroeder,et al.  The Visualization Toolkit , 2005, The Visualization Handbook.

[3]  J. Boone,et al.  Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities. , 2013, Physics in medicine and biology.

[4]  John M Boone,et al.  Methodology for generating a 3D computerized breast phantom from empirical data. , 2009, Medical physics.

[5]  B. Gusterson,et al.  Human Breast Development , 2004, Journal of Mammary Gland Biology and Neoplasia.

[6]  Ken Perlin,et al.  Improving noise , 2002, SIGGRAPH.

[7]  A. Burgess,et al.  Human observer detection experiments with mammograms and power-law noise. , 2001, Medical physics.

[8]  J. Going,et al.  Escaping from Flatland: clinical and biological aspects of human mammary duct anatomy in three dimensions , 2004, The Journal of pathology.

[9]  Hang Si,et al.  TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator , 2015, ACM Trans. Math. Softw..

[10]  Benjamin J. Ellis,et al.  FEBio: finite elements for biomechanics. , 2012, Journal of biomechanical engineering.

[11]  Ioannis A. Kakadiaris,et al.  Modeling for Plastic and Reconstructive Breast Surgery , 2000, MICCAI.

[12]  K Bliznakova,et al.  A three-dimensional breast software phantom for mammography simulation. , 2003, Physics in medicine and biology.

[13]  Andrew D. A. Maidment,et al.  Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm. , 2011, Medical physics.