The mammary parenchyma is a complex arrangement of tissues that can greatly vary among individuals, potentially masking cancers in breast screening images. In this work, we propose a Simplex-based method to simulate anatomical patterns and textures seen in digital breast tomosynthesis. Our approach involves selecting appropriate Simplex noise parameters to represent distinct categories of breast parenchyma with variable volumetric breast density (%VBD). We use volumetric coarse masks (70 × 60 × 50 mm3) to outline patches of both dense and adipose tissues. These masks serve as a foundation for volumetric and multi-scale Simplex-based noise distributions. The Simplex-based noise distributions are normalized and thresholded using gradient level sets selected to binarize specific Simplex frequencies. The Simplex frequencies are summed and binarized using post-hoc thresholds, resulting in patches of tissue tailored to represent anatomic-like structures seen in digital breast tomosynthesis (DBT) images. We simulate DBT projections and reconstructions of the patches of breast tissue following the acquisition geometry and exposure settings of a clinical tomosynthesis system. We calculate the power spectra and estimate the power-law exponent (β) using a sample of DBT reconstructions (n=500, equally stratified by four density classes). Our findings reveal an absolute β value of 3.0, indicative of the improvements achieved in both the performance and realism of the breast tissue simulation. In summary, our proposed Simplex-based method enhances realism and texture variations, ensuring the presence of anatomical and quantum noise at levels consistent with the image quality expected in breast screening exams.
KEYWORDS: Breast, Digital breast tomosynthesis, 3D modeling, Skin, Fractal analysis, Tissues, Prototyping, Computer simulations, Signal attenuation, Principal component analysis
Virtual clinical trials (VCTs) have been used widely to evaluate digital breast tomosynthesis (DBT) systems. VCTs require realistic simulations of the breast anatomy (phantoms) to characterize lesions and to estimate risk of masking cancers. This study introduces the use of Perlin-based phantoms to optimize the acquisition geometry of a novel DBT prototype. These phantoms were developed using a GPU implementation of a novel library called Perlin-CuPy. The breast anatomy is simulated using 3D models under mammography cranio-caudal compression. In total, 240 phantoms were created using compressed breast thickness, chest-wall to nipple distance, and skin thickness that varied in a {[35, 75], [59, 130), [1.0, 2.0]} mm interval, respectively. DBT projections and reconstructions of the phantoms were simulated using two acquisition geometries of our DBT prototype. The performance of both acquisition geometries was compared using breast volume segmentations of the Perlin phantoms. Results show that breast volume estimates are improved with the introduction of posterior-anterior motion of the x-ray source in DBT acquisitions. The breast volume is overestimated in DBT, varying substantially with the acquisition geometry; segmentation errors are more evident for thicker and larger breasts. These results provide additional evidence and suggest that custom acquisition geometries can improve the performance and accuracy in DBT. Perlin phantoms help to identify limitations in acquisition geometries and to optimize the performance of the DBT prototypes.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.