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: Image segmentation, Breast, Tissues, Cancer, Digital breast tomosynthesis, Computer simulations, Signal to noise ratio, Signal attenuation, Motion models, X-rays
Our lab has built a next-generation tomosynthesis (NGT) system utilizing scanning motions with more degrees of freedom than clinical digital breast tomosynthesis systems. We are working toward designing scanning motions that are customized around the locations of suspicious findings. The first step in this direction is to demonstrate that these findings can be detected with a single projection image, which can guide the remainder of the scan. This paper develops an automated method to identify findings that are prone to be masked. Perlin-noise phantoms and synthetic lesions were used to simulate masked cancers. NGT projections of phantoms were simulated using ray-tracing software. The risk of masking cancers was mapped using the ground-truth labels of phantoms. The phantom labels were used to denote regions of low and high risk of masking suspicious findings. A U-Net model was trained for multiclass segmentation of phantom images. Model performance was quantified with a receiver operating characteristic (ROC) curve using area under the curve (AUC). The ROC operating point was defined to be the point closest to the upper left corner of ROC space. The output predictions showed an accurate segmentation of tissue predominantly adipose (mean AUC of 0.93). The predictions also indicate regions of suspicious findings; for the highest risk class, mean AUC was 0.89, with a true positive rate of 0.80 and a true negative rate of 0.83 at the operating point. In summary, this paper demonstrates with virtual phantoms that a single projection can indeed be used to identify suspicious findings.
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.
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