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DigiBreast - A Complex Digital Breast Phantom with Clinically-Derived Tissue Compositions


  • Release date: work-in-progress
  • Created by: Bin Deng <bdeng1 at nmr.mgh.harvard.edu>, Qianqian Fang <fangq at nmr.mgh.harvard.edu>
  • License: The DigiBreast phantom main and source data are in the public domain; the MATLAB scripts are covered under the BSD license. Please refer to the LICENSE_BSD.txt for proper use and redistribution of the contents.

1. Download
2. Introduction
3. What's in the package
3.1. Folder Structure
3.2. DigiBreast Data
3.3. DigiBreast Source Data
3.4. Script
4. Tissue optical properties
5. Footnote
6. Reference

1. Download

Coming soon…

2. Introduction

3. What's in the package

3.1. Folder Structure

The DigiBreast package is comprised of the "data" folder and the "script" folder, together with related documentation. The package folder/file structure is explained below.

DigiBreast
├── AUTHORS.txt                      # Acknowledgement of contributions
├── README.txt                       # This file
├── data                             # DigiBreast data in MATLAB .mat format
│   └── DigiBreast.mat                 # DigiBreast main data
│   └── DigiBreast_source.mat          # DigiBreast source data
├── script                           # All related MATLAB scripts
│   ├── digibreast_meshrefine.m        # Creating the refined meshes at a given ROI
│   ├── digibreast_savejson.m          # Saving DigiBreast data in JSON and UBJSON
│   ├── digibreast_priors.m            # Creating tissue compositional maps
│   ├── digibreast_lesionprofile.m     # Creating a Gaussian-spherical tumor prior
│   └── digibreast_tablelookup.m       # Utility to lookup the optical properties
└── LICENSE_BSD.txt                  # License file

3.2. DigiBreast Data

DigiBreast.mat is a MAT-file that contains all essential components of the 3D digital breast phantom used in the simulation study as presented in Deng2015 paper, including variables ForwardMesh, ReconMesh, LesionCentroids, and OpticalProperties. This phantom is built on the source images included in DigiBreast_source.mat, and a 2 cm slab was added toward the chest wall.

ForwardMesh
a 1x1 structure contains three fields, namely "node","elem",and "glandularity".

  • ForwardMesh.node:
  • ForwardMesh.elem:
  • ForwardMesh.glandularity:
    • ForwardMesh.glandularity.truth:
    • ForwardMesh.glandularity.empirical:
    • ForwardMesh.glandularity.dualgaussian:
    • ForwardMesh.glandularity.thresholdp2:
    • ForwardMesh.glandularity.threshold2:

ReconMesh
a 1x1 structure contains two fields, namely "node" and "elem".
  • ReconMesh.node:
  • ReconMesh.elem:

LesionCentroids
a 1x1 structure with two fields, "adipose" and "fibroglandular", containing the [x,y,z] lesion centroids (in mm) of the two simulated lesion locations (as used in the Deng2015 paper) within either adipose or fibroglandular tissue vicinity.

OpticalProperties

3.3. DigiBreast Source Data

DigiBreast_source.mat is a MAT-file that contains the anonymized and down-sampled (to 1 mm pixel resolution) 2D images of the original mammogram, glandularity and thickness maps. All original images are clinical measurements of a real patient breast in cranio-caudal view (CC view) using a Philips dual-energy mammographic system -- MicroDose SI. The MAT-file includes variables Mammogram, Glandularity, ThicknessMap, and Registration. Users can choose to use our readily-built 3D DigiBreast phantom in DigiBreast.mat, or to use the 2D source images offered in DigiBreast_source.mat to customize their own 3D realistic breast phantoms.

Mammogram
Clinically measured mammogram in CC view (1 mm resolution). The mammogram has been masked to exclude skin region.

Glandularity
Fibroglandular tissue volume fraction map (1 mm resolution) derived directly from clinical measurement. This is the "ground truth" glandularity referred in the Deng2015 paper. By mapping this Glandularity map onto the forward mesh of the DigiBreast mesh, the variable ForwardMesh.glandularity.truth in DigiBreast.mat is derived.

ThicknessMap
Distribution of the breast thickness (1 mm resolution).

Registration
12 x 3 matrix representing the registration between mammogram image space and optical measurement space for multi-modal imaging purposes.

3.4. Script

digibreast_lesionprofile.m
Generate the Gaussian sphere lesion profile at defined centroid.
 % example:
 % to generate a Gaussian lesion profile that represents the volume fractions of a 5mm FWHM size lesion located within the adipose vicinity as shown in [Deng2015] on the forward mesh
   node=ForwardMesh.node;
   centroid=LesionCentroids.adipose;
   fwhmsize=5;
   lesionprofile=digibreast_lesionprofile(node,centroid,fwhmsize);
   figure;plotmesh([ForwardMesh.node lesionprofile],ForwardMesh.elem,'z=15','linestyle','none');colorbar;

digibreast_meshrefine.m
Refine the input mesh within a spherical region centered at centroid.
 % example:
 % to generate the refined mesh used in [Deng2015] (see Table 1 for details)
   mesh=ForwardMesh;
   mesh_refined=digibreast_meshrefine(mesh,LesionCentroids.adipose,10,0.1);
   plotmesh(mesh_refined.node,mesh_refined.elem,'z=15','facecolor','w');
   reconmesh_refined=digibreast_meshrefine(ReconMesh,LesionCentroids.adipose,10,1);
 % interpolation of glandularity maps in the refined mesh
   mesh.value=[ForwardMesh.glandularity.truth ForwardMesh.glandularity.dualgaussian];
   mesh_refined=digibreast_meshrefine(mesh,LesionCentroids.adipose,10,0.1);
   figure; 
   subplot(121);plotmesh([mesh_refined.node mesh_refined.value(:,1)],mesh_refined.elem,'z=15','linestyle','none');colorbar
   subplot(122);plotmesh([mesh_refined.node mesh_refined.value(:,2)],mesh_refined.elem,'z=15','linestyle','none');colorbar

digibreast_priors.m
Generate tissue compositional priors for the DigiBreast phantom.
 % example:
 % to generate 2-compositional normal tissue priors using glandularity map derived from dual gaussian segmentation algorithm
   priors=digibreast_priors(ForwardMesh.glandularity.dualgaussian);
   figure;
   subplot(211);plotmesh([ForwardMesh.node priors.normal(:,1)],ForwardMesh.elem,'z=15','linestyle','none');title('Adipose tissue volume fractions');colorbar;
   subplot(212);plotmesh([ForwardMesh.node priors.normal(:,2)],ForwardMesh.elem,'z=15','linestyle','none');title('Fibroglandular tissue volume fractions');colorbar;
 % to generate 3-compositional normal and lesion tissue priors using the same glandularity map derived from dual gaussian segmentation algorithm
   lesionprofile=digibreast_lesionprofile(ForwardMesh.node,LesionCentroids.adipose,5);
   priors=digibreast_priors(ForwardMesh.glandularity.dualgaussian,lesionprofile);
   figure;
   subplot(311);plotmesh([ForwardMesh.node priors.lesion(:,1)],ForwardMesh.elem,'z=15','linestyle','none');title('Adipose tissue volume fractions');colorbar;
   subplot(312);plotmesh([ForwardMesh.node priors.lesion(:,2)],ForwardMesh.elem,'z=15','linestyle','none');title('Fibroglandular tissue volume fractions');colorbar;
   subplot(313);plotmesh([ForwardMesh.node priors.lesion(:,3)],ForwardMesh.elem,'z=15','linestyle','none');title('Lesion tissue volume fractions');colorbar;

digibreast_savejson.m
Export DigiBreast mesh data into JSON and UBJSON files.

digibreast_tablelookup.m
Search optical properties from the OpticalProperties table by tissue and property names.
 % example:
   Sp_fib=digibreast_tablelookup(OpticalProperties,'fib','s_power');

4. Tissue optical properties

With the provided fibroglandular tissue volume fraction map (variable Glandularity_Truth within DigiBreast_source.mat), users can freely build your own breast phantom by multiplying the optical properties of fibroglandular, adipose, and cancerous tissues to the volume fractions at each pixel/node.


Tissue type HbO (μM) HbR (μM) μs’ (mm−1)
at 690 nm at 830 nm
Adipose 13.84 4.81 0.851 0.713
Fibroglandular 18.96 6.47 0.925 0.775
Malignant 20.60 6.72 0.957 0.801

5. Footnote

  • The DigiBreast phantom main and source data is in the public domain. The MATLAB scripts under the "script" sub-folder have a BSD license. See LICENSE_BSD.txt for details.

  • Some of the scripts included in this package requires the installation of the "iso2mesh" and "jsonlab" toolboxes. To download these toolboxes:

  • If you use this DigiBreast phantom main or source data in your publication, please cite the phantom version number (currently Version_1) to avoid conflict to any further updates of this mesh. We are also appreciated if you can cite the Deng2015 paper below.

6. Reference

[Deng2015] B. Deng, D. H. Brooks, D. A. Boas, M. Lundqvist, and Q. Fang, "Characterization of structural-prior guided optical tomography using realistic breast models derived from dual-energy x-ray mammography," Biomedical Optics Express 6(7): 2366-79 (2015). doi: 10.1364/BOE.6.002366

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