Can digital breast tomosynthesis improve identification of malignant calcifications?

A Novel Task-Based reconstruction approach for digital breast tomosynthesis

The reconstruction of a volumetric image from Digital Breast Tomosynthesis (DBT) measurements is an ill-posed inverse problem, for which existing iterative regularized approaches can provide a good solution. However, the clinical task is somehow omitted in the derivation of those techniques, although it plays a primary role in the radiologist diagnosis. In this work, we address this issue by introducing a novel variational formulation for DBT reconstruction, tailored for a specific clinical task, namely the detection of microcalcifications. Our method aims at simultaneously enhancing the detectability performance and enabling a high-quality restoration of the background breast tissues. Our contribution is threefold. First, we introduce an original task-based reconstruction framework through the proposition of a detectability function inspired from mathematical model observers. Second, we propose a novel total-variation regularizer where the gradient field accounts for the different morphological contents of the imaged breast. Third, we integrate the two developed measures into a cost function, minimized thanks to a new form of the Majorize Minimize Memory Gradient (3MG) algorithm. We conduct a numerical comparison of the convergence speed of the proposed method with those of standard convex optimization algorithms. Experimental results show the interest of our DBT reconstruction approach, qualitatively and quantitatively.

Optimization of the Radiation Dose of Digital Breast Tomosynthesis in Opportunistic Screening by Studying the Effect of Different Combinations of FFDM and DBT Views

Background

Full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) are used separately or in combination to identify small breast lesions. The dose of the examination depends on the density of the breast and the imaging (FFDM or DBT) performed. We have performed a retrospective review of FFDM and DBT in women with denser breasts in order to demonstrate how varying the combination of FFDM and DBT in CC and MLO views affects lesion detection and the average gland dose.

Methods

Eighty-one patients with dense breast received both full-field digital mammography (FFDM) and DBT bilateral screening. The recorded data included the display rates for small lesions or other positive lesions, the type of breast gland, the average gland dose (AGD), and the compression thickness of different collection methods. ANOVA was used to compare the AGD among different collection combinations, and t-test was used to perform pairwise comparison between groups with the same gland type. The relationship between AGD and compression thickness was analyzed by Pearson linear correlation, and the lesion display rates were compared using Chi-square test.

Results

We found that AGDs were significantly different among the 6 collection methods (F = 119.06, p<0.01), but were not obviously different between groups with the same gland type (F = 0.848, p>0.05). The types of dense glands were correlated with compression thickness, and the thickness was moderately to strongly correlated with AGD (r=0.596-0.736). The combination of single-view DBT(CC-DBT) and FFDM showed significantly higher mass display rates than the two-view FFDM (p<0.05), while the display rates for other positive lesions were similar (p>0.05).

Conclusion

Our study showed that in opportunistic screening of patients with small breast masses that can be easily detected by ultrasound, MLO-FFDM+CC-DBT or CC-FFDM+MLO-DBT combinations can better balance the individual average gland dose and detection accuracy. The study result cannot be applied to the detection of non-mass lesions as the numbers are too small.