In the United States, one in eight women could come to suffer from breast cancer within their lifetime and undergo a mastectomy, the surgical removal of the breast(s). Those women who undergo a mastectomy are often concerned with their appearance after the operation, as well as the overall functioning of their body. Breast reconstruction using the patients’ own tissue is typically required after a mastectomy, and some specific information is necessary to understand a woman’s surgical options and their appearance after surgery.
In a recent article, researchers at The University of Texas describe a new approach to quantifying a patient’s apperance via imaging and 3D modeling techniques by combining 2D imaging.
In their research, they analyzed both 2D and 3D surface images to extract relevant information about appearance changes in order to develop patient-specific mechanistic models to predict realistic outcomes. They achieved the 3D surface imaging via a stereophotography system from 3dMD, a medical imaging company in Atlanta, GA, and measured properties such as curvature and volume that are more difficult to assess from 2D imaging. When they capture the images, they computed measurements of breast morphology such as symmetry using distances between fiducial points including the nipples, the sternal notch, and the umbilicus. They also developed a semi-automated algorithm for registration of multiple images taken over time based on the assumption that the skeleton is relatively stable and thus the transformation can be treated as rigid.
Clement S. Sun, Mia K. Markey, Fatima A. Merchant, Krishnaswamy Ravi-Chandar, Michelle C. Fingeret and Gregory P. Reece — the research and development team for this project, which hail from UT Austin, University of Houston, and MD Anderson — explain in detail their approach in an article on spie.org how, “imaging in 2D that consists of clinical photographs with a standardized background and poses is currently the customary practice for documenting surgical outcomes.” This new approach, however, utilizes the 3D surface imaging in order to make determinations about changes in breast morphology that takes place throughout the breast reconstruction process. In order to accomplish this successfully, the solution’s 3D images are plotted along a timeline as well, which is powered by, “. . . a semi-automated algorithm for registration of multiple images taken over time based on the assumption that the skeleton is relatively stable and thus the transformation can be treated as rigid.
The result of this technology is not a mere “mock-up” or “best guess” of a reconstructed breast, but rather a true “physics-based, patient-specific biomechanical model that uses the 3D geometry obtained via 3D surface imaging combined with the material properties of the breast to predict breast reconstruction post-operative results (see Figure 2 from the source article). This of course is a major advancement, since the 3D images take into account additional dimensions that comprise a real human breast, such as the thickness of skin, skin deformations, and musculature as well.
The team’s end goal is to streamline their analyses through techniques such as development of algorithms that automatically align images to a defined orientation and locate fiducial points, and to provide highly valuable quantitative feedback to the patients.
Photo from spie.org