Building A Virtual Breast Elastography Phantom Lab Using Open Source Software
Yu Wang, Emily Helminen and Jingfeng Jiang*, Department of Biomedical Engineering
Michigan Technological University, Houghton, MI 49931, USA
4. Results
1. Background
 According to statistics from the American Cancer Society, breast cancer is the second most common form of cancer in American
women (after skin cancer) and the second most common cause of death (after lung cancer).
 Breast tumors are often detected by imaging, but current imaging methods are prone to low specificity.
 Uncertainties in imaging diagnosis have led to a high rate (approximately 75%) of benign biopsies, thereby placing a major socio-economic
burden on the American healthcare system and becoming a source of distress for patients.
 In recent years, Ultrasound-based Elastography (UE) has been successfully applied to non-invasive differentiation of in vivo breast lesions,
though early results demonstrate that there is a lack of confidence in image details of UE in the clinical community.
 Confidence on image interpretations can only be gained through rigorous validation of image pixel values with the underlying soft tissue
properties (i.e. “ground truth”) in complex, heterogeneous but known media.
2. Objectives
EXAMPLE 1. Simulated complex patterns in
heterogeneous media
*NOTE: Three volumes were selected from the virtual breast phantom (Fig.
1 (a)) in this example. Only the center plan of the simulated impendance
field (volume) was shown, the impedance unit is kg/(s·m2).
Fig. 4. Hypo-echoic breast lesion. (1) Lesion. (2) Fibro-glandular. (3) Breast
fat. (4). Cooper’s ligament. (5) Milk duct.
 Build a virtual breast phantom lab in the public domain used for rigorous imaging testing.
 Two examples are used to show the capabilities of this virtual breast phantom lab.
A sophisticated acoustic simulation based on three complex but known media.
An Elastographic application based on an un-deformed and deformed breast phantom.
3. Materials and Methods
(1) Steps of building virtual phantom lab
Fig. 1. An illustration of the steps in our ultrasound simulation based on
virtual breast phantom. The geometry of pre- (a) and post- (b) deformed
breast phantom are created first, which are meshed to tetrahedrons (c) in the
second step. In third step, the meshed breast phantom is re-sampled to
rectilinear grid (d). Then acoustic parameters are assigned to rectilinear grid for
K-wave (e). Finally, B-mode images (f) (g) can be obtained. The deformation
area is marked using white circle in (b).
 White---Breast skin
 Blue---Cooper’s ligament
 Red---Fibro-glandular
 Green---Milkduct
 Orange---Breast fat
 Maroon ---Lesion
Fig. 2. An illustration of the phantom. Compositions may vary as needed.
 Geometry Creation (Fig. 1 (a))
 Through image segmentation, landmark geometries such as
the skin layer and fibro-glandular layer (Fig. 3 (b)) were created
using magnetic resonance imaging (MRI) data from a female
cadaver in the Visible Human Project. This step was done using
ScanIP.
 Small random tissue structures such as milk ducts (Fig. 3 (c))
and Cooper’s ligaments were created based on in-house
algorithm derived from VTK.
 Mesh Generation (Fig. 1 (c))
An open source mesh generator Tetgen was used to generate
high quality tetrahedral meshes.
Nearly 2 million finite (tetrahedral) elements were generated.
 Acoustic Simulation (Fig. 1 (d))
Since K-wave is a k-space finite difference method, the
meshed breast phantom was re-sampled to rectilinear grid. Speed
of sound (SOS) and density from different materials were
asigned to these rectilinear grids. Six materials in this virtual
breast phantom were shown in Fig. 2. This step was done using
VTK.
 Biomechanical Simulation (Fig. 1 (b))
To realistically simulate tissue deformations to mimic quasistatic elastography, a clinical setting linear transducer is first in
contact with the skin and is then used to push the breast tissue
downward to deform this breast phantom. Boundary conditions
were applied to mimic this clinical scanning condition. This
finite element results were solved using FEBio.
A small patch (approxiately 4cm×1.5cm) was selected and
1mm displacement (1.5% axial compression) was applied.
 Running Simulation (Fig. 1 (e))
During the acoustic K-WAVE simulation, 256 lines were used
to cover the entire field of view (approximately 36 mm).
(2) Parameters of simulated linear transducer
center frequency---6MHz
bandwidth---80%
element width---0.15mm
element height---5mm
attenuation coefficient---0.75
dB/MHz/com
attenuation power
w/frequency ---1.5
B/A ---6
Fig. 3. Geometries of two structures in virtual breast phantom.
(a) A breast MRI, from W. Huang, WM Keck Foundation High-Field MRI-AIRC at the OHSU, Portland. The white area in this figure is fibro-glandular.
(b) Fibro-glandular geometry segmented from MRI data. It is similar to white area in Fig. 3 (a).
(c) Milkduct geometry created by VTK.
Fig. 5. Milk-duct. (1) Breast fat. (2) Fibro-glandular. (3) Milk duct. (4) Lobe.
Fig. 8. Result of Quasi-static Elastography for simulated breast lesion in
Fig. 7(a). (a)(b) and (c) were estimated axial displacements in three
conditions: (1) axial displacement only; (2) all displacements; and (3) axial,
lateral and tripled elevational displacements. (a)-(c) were the estimated axial
displacements under three conditions, respectively, while (d)-(f) were
corresponding strain images, respectively.
Three different conditions were compared to show changes among
estimated axial displacements caused by non-axial deformations.
From conditions 1-3, the estimated correlation between pre- and
motion-compensated post-deformation RF echo signals dropped from
0.81 (condition 1) to 0.78 (condition 2) and then to 0.70 (condition 3).
All the estimated axial displacement distributions were similar to the
true axial displacement (Fig. 7 (b)). And the estimated strain images
were consistent with the tissue landmarks visible in the corresponding
B-mode image (Fig. 7 (a)) but more complex.
5. Conclusions
Fig. 6. Cooper’s ligaments. (1) Lesion. (2) Fibro-glandular. (3) Cooper’s
ligament. (4) Breast fat.
EXAMPLE 2. Quasi-static Elastography
*NOTE: Tissue displacement in region of interest (ROI) was estimated by
comparing pre- and post- deformed echo signals using motion tracking
method.
Based on our preliminary study, the proposed virtual breast
phantom can be used to perform sophisticated acoustic simulations
and can also be used in quasi-static elastography applications.
 In example 1, simulated B-mode images can represent the
complex but known medium shown in breast phantom.
 In example 2, estimated stain images were consistent with the
underlying tissue elasticity. Our results also suggested that
accuracy of estimated displacements was affected by the
degree of non-axial deformation.
6. Future work
 Incorporate more anatomical structures in order to increase the
realism of the breast model.
Make the code for the virtual phantom lab widely available for
anyone to use.
Contact information
Fig. 7. (a) B-mode image including the region of interest (ROI) (white
rectangle). (b) (c) (d) are the corresponding true displacements along axial,
lateral and elevational directions caused by 1.5% axial compression in ROI.
The displacement color bars are in millimeters. The arrows point to the
position of lesion.
*NOTE:
ScanIP (Simpleware Inc., Exter, United Kingdom)
VTK (Visualization Toolkit, Kitware Inc., NY, USA)
FEBio (Finite Elements for Biomechanics, University of Utah, UT, USA)
Tetgen (Open source mesh generator, version 1.4.2; WIAS, Berlin,
Germany)
K-Wave (Matlab package used for ultrasound simulation, www.k-wave.org)
Jingfeng Jiang, PhD
Material and Mineral Engineering Building 309,
Department of Biomedical Engineering,
Michigan Technological University
Houghton, MI 49931
Voice: (906)-487-1943, Fax:
(906)-487-1717
Email: jjiang1@mtu.edu
Acknowledgments
This project was partially supported by the Michigan Technology
University.