Spatial dependency of lesion detectability in digital breast tomosynthesis.

X-ray imaging results in inhomogeneous irradiation of the detector and distortion of structures in the periphery of the image; yet the spatial dependency of tomosynthesis image-quality metrics has not been extensively investigated. In this study, we use virtual clinical trials to quantify the spatial dependency of lesion detectability in our lab's next-generation tomosynthesis (NGT) system. Two geometries were analyzed: a conventional geometry with mediolateral source motion, and a NGT geometry with T-shaped motion. Breast parenchymal texture was simulated using an open-source library with Perlin noise using 400 random seeds and three breast densities. Spherical mass lesions were inserted in the central slice of the phantoms using the voxel additive method. Image acquisition was simulated using in-house ray-tracing software and simple backprojection was performed using commercial reconstruction software. Lesion detectability with Channelized Hotelling Observers (CHOs) was analyzed using receiver operating characteristic curves to measure the detectability index (d') at 154 unique locations for the lesions. We also divided images into three non-overlapping regions (differing in terms of distance from the chest wall). At the 0.05 level of significance, there was a statistically significant difference between the geometries in terms of d' in one of the three regions, with the T geometry offering superior detectability. Examining all 154 lesion locations, the T geometry was found to offer lower spread (standard deviation) in d' values throughout the image area, and superior d' at 83 of 154 locations (53.9%). In summary, the T geometry enables superior lesion detection and mitigates anisotropies.

Multiclass Segmentation of Suspicious Findings in Simulated Breast Tomosynthesis Images Using a U-Net.

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.