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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
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.
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.
% 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;
% 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
% 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;
% example: Sp_fib=digibreast_tablelookup(OpticalProperties,'fib','s_power');
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 |
[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
[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). doi:10.1148/radiol.10082176