__________________________________________________________________
DigiBreast
A Complex Digital Breast Phantom with 3D Tissue Compositions
__________________________________________________________________
DigiBreast
* Version: 1.0
* Release date: July 6, 2015
* Created by: Bin Deng and Qianqian Fang
{bdeng1,fangq}@nmr.mgh.harvard.edu
* License: The DigiBreast phantom 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. [2]Download
2. [3]Introduction
3. [4]What's in the package
3.1. [5]Folder structure
3.2. [6]DigiBreast phantom data
3.3. [7]DigiBreast source data
3.4. [8]Scripts
4. [9]Tissue optical properties
5. [10]Footnote
6. [11]Reference
1. Download
Please download the latest release (Version 1) at our
[12]registration/download page.
2. Introduction
DigiBreast is a numerical breast phantom designed for 3D multi-physics
simulations and validations of model-based image reconstruction
algorithms for mammographically compressed breasts. The development of
this phantom was described in [13]Deng2015 with the original intent of
testing a structure-prior guided image reconstruction algorithm for
combined x-ray mammography and diffuse optical tomography (DOT)
imaging. This digital breast phantom contains generic information such
as 3D breast shapes and internal anatomical structures. We believe such
breast phantom can address the needs for simulation-based validations
for a wide range of model-based imaging modalities. Potential utilities
of this digital phantom include, but not limited to, simulations of
breast deformation, 2D and 3D x-ray breast imaging, and tomographic
imaging of a compressed breast using tomographic optical, microwave,
thermal and electrical impedance methods.
A unique aspect of this digital breast phantom is the inclusion of a
realistic 3D glandularity map measured through a dual-energy x-ray
mammography system, provided by Philips Healthcare. In comparison,
conventional numerical breast phantoms represent various breast tissue
constituents, i.e. the fibroglandular and adipose tissue, by
piece-wise-constant regions (using a binary segmentation algorithm).
Such representation removes the fine spatial details in the breast
anatomical images, and results in loss of information. Statistical, or
fuzzy segmentation methods avoid such information loss, and provide
spatially-varying tissue volume fraction maps. In our previous works
[14]Fang2010, we have reported a joint x-ray/DOT image reconstruction
algorithm utilizing a spatially varying tissue compositional model to
improve DOT image resolution. This method was further characterized in
[15]Deng2015.
The DigiBreast phantom has limitations. While the breast shape is in
3D, the internal tissue compositional maps were derived from 2D x-ray
measurements, thus, have an overall "cylindrical" shape along the
sagittal direction. We, however, believe this approximation has
negligible impact to most potential applications which deal with a
mammographically compressed breast. This is because most of these
methods utilizing a parallel-plate based measurement scheme and such
scheme has an anisotropic spatial resolution - the horizontal/axial has
significantly higher resolution than in the vertical/sagittal
direction. Therefore, the focus in most of these imaging modalities are
in the axial/horizontal view instead of the sagittal view. This
limitation can be overcome in the future when 3D x-ray spectral imaging
becomes available.
3. What's in the package
3.1. Folder structure
The DigiBreast package contains a "data" folder and a "script" folder,
along with related documentation. In the JSON and UBJSON formatted
packages, the data folder is replaced by either "json" or "ubjson". In
either folder, MATLAB variables that encode the DigiBreast Phantom are
saved as separate JSON and UBJSON formatted data files. These files can
be loaded into MATLAB using the free JSONLab toolbox
([16]http://iso2mesh.sf.net/jsonlab).
The package 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
├── json # DigiBreast data in JSON format (optional)
│ └── <<VariableName>>.json # JSON files for each MATLAB variable
├── ubjson # DigiBreast data in UBJSON format (optional)
│ └── <<VariableName>>.ubj # UBJSON files for each MATLAB variable
├── 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 phantom data
DigiBreast.mat is a MATLAB mat-file containing all essential components
of the 3D digital breast phantom used in the simulation study as
presented in the [17]Deng2015 paper. It contains 4 data structures -
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 MATLAB structure containing three fields, namely "node",
"elem",and "glandularity".
* ForwardMesh.node: the node coordinate list of the forward mesh
* ForwardMesh.elem: the tetrahedral element list of the forward mesh
* ForwardMesh.glandularity: a struct containing the following fields:
+ ForwardMesh.glandularity.truth: the measured glandularity at
each node
+ ForwardMesh.glandularity.empirical: the nodal glandularity
list using an empirical segmentation algorithm
+ ForwardMesh.glandularity.dualgaussian: the nodal glandularity
list using a dual-gaussian segmentation method
+ ForwardMesh.glandularity.thresholdp2: the nodal glandularity
list using a threshold segmentation method with a 0.2%
threshold
+ ForwardMesh.glandularity.threshold2: the nodal glandularity
list using a threshold segmentation method with a 2% threshold
ReconMesh
a MATLAB structure containing two fields, namely "node" and
"elem".
* ReconMesh.node: the node coordinate list of the reconstruction mesh
* ReconMesh.elem: the tetrahedral element list of the reconstruction
mesh
LesionCentroids
a MATLAB 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
[18]Deng2015 paper) within either adipose or fibroglandular
tissue vicinity.
OpticalProperties
a 4x9 cell array with optical properties (HbO, HbR, HbT, SO2,
scattering power and amplitude, reduced scattering coefficients
at 690 nm and 830 nm) of adipose and fibroglandular tissues, as
well as of malignant lesions. These optical properties are
estimated based on mean values of reconstruction optical images
for our previous clinical study published in [19]Fang2011.
Optical properties are all properly labeled within the variable,
and should be easy to interpret. A function included in this
package, "digibreast_tablelookup.m", can also be used to look up
for any particular optical properties of a certain tissue type.
3.3. DigiBreast source data
DigiBreast_source.mat is a MATLAB mat-file that contains the anonymized
and down-sampled (1 mm pixel resolution) 2D images of the original
mammogram, glandularity and thickness maps. All original images are
clinical measurements from a normal breast in the 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 create
their own 3D realistic breast phantoms using different meshing settings
based on the 2D source images in DigiBreast_source.mat.
Mammogram
A digital breast mammogram in the CC view (335x307 in 1x1 mm
pixels). The mammogram has been masked to exclude skin region.
Glandularity
Fibroglandular tissue volume fraction map (335x307 in 1x1 mm
pixels) derived directly from the MicroDose SI measurement. This
is the "ground truth" glandularity referred in the [20]Deng2015
paper. By stacking vertically and repeating this image, we can
map the forward mesh nodes into this 3D glandularity profile
using the Registration data structure below, and produce the
subfield ForwardMesh.glandularity.truth in DigiBreast.mat.
ThicknessMap
the measured breast thickness map at each pixel location
(335x307 in 1x1 mm pixels).
Registration
a 12 x 3 matrix representing the mapping between the mammogram
image space and optical probe space for multi-modal imaging
purposes. The odd-numbered rows are 6 key-points (x/y/z
coordinates) in the mammogram-voxel-space, and the even-numbered
rows are the corresponding key-points in the optical probe space
(the same as the mesh coordinate space).
3.4. Scripts
digibreast_lesionprofile.m
Generate a Gaussian-sphere lesion profile at defined centroid.
Example
To generate a Gaussian lesion profile that represents the volume
fractions of a 5 mm FWHM lesion located within the adipose vicinity as
shown in [21]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 [22]Deng2015 (see Table 1 in the
paper 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 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:
* iso2mesh: [23]http://iso2mesh.sourceforge.net/
* JSONLab [24]http://iso2mesh.sourceforge.net/jsonlab
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. If you use
DigiBreast data in your research, the authors are appreciated if you
can cite the [25]Deng2015 paper below in your related publications.
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).
[Fang2010] Q. Fang, R.H. Moore, D. B. Kopans, D.A. Boas DA,
“Compositional-prior-guided image reconstruction algorithm for
multi-modality imaging,” Biomedical Optics Express, 1(1), 223-235
(2010)
[Fang2011] Q. Fang, J. Selb, S.A. Carp, G. Boverman, E.L. Miller, D.H.
Brooks, R.H. Moore, D.B. Kopans and D.A. Boas, "Combined optical and
X-ray tomosynthesis breast imaging," Radiology 258(1): 89-97 (2011).
References
1. http://mcx.sourceforge.net/cgi-bin/index.cgi?action=rss
2. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Download
3. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Introduction
4. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#What_s_in_the_package
5. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Folder_structure
6. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#DigiBreast_phantom_data
7. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#DigiBreast_source_data
8. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Scripts
9. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Tissue_optical_properties
10. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Footnote
11. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Reference
12. http://mcx.sourceforge.net/cgi-bin/index.cgi?register/digibreast
13. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
14. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Fang2010
15. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
16. http://iso2mesh.sf.net/jsonlab
17. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
18. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
19. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Fang2011
20. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
21. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
22. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
23. http://iso2mesh.sourceforge.net/
24. http://iso2mesh.sourceforge.net/jsonlab
25. http://mcx.sourceforge.net/cgi-bin/index.cgi?DigiBreast#Deng2015
