New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers

Abstract

Background The image quality of digital breast tomosynthesis (DBT) volumes depends greatly on the reconstruction algorithm. Purpose To compare two DBT reconstruction algorithms used by the Siemens Mammomat Inspiration system, filtered back projection (FBP), and FBP with iterative optimizations (EMPIRE), using qualitative analysis by human readers and detection performance of machine learning algorithms. Material and Methods Visual grading analysis was performed by four readers specialized in breast imaging who scored 100 cases reconstructed with both algorithms (70 lesions). Scoring (5-point scale: 1 = poor to 5 = excellent quality) was performed on presence of noise and artifacts, visualization of skin-line and Cooper's ligaments, contrast, and image quality, and, when present, lesion visibility. In parallel, a three-dimensional deep-learning convolutional neural network (3D-CNN) was trained (n = 259 patients, 51 positives with BI-RADS 3, 4, or 5 calcifications) and tested (n = 46 patients, nine positives), separately with FBP and EMPIRE volumes, to discriminate between samples with and without calcifications. The partial area under the receiver operating characteristic curve (pAUC) of each 3D-CNN was used for comparison. Results EMPIRE reconstructions showed better contrast (3.23 vs. 3.10, P = 0.010), image quality (3.22 vs. 3.03, P < 0.001), visibility of calcifications (3.53 vs. 3.37, P = 0.053, significant for one reader), and fewer artifacts (3.26 vs. 2.97, P < 0.001). The 3D-CNN-EMPIRE had better performance than 3D-CNN-FBP (pAUC-EMPIRE = 0.880 vs. pAUC-FBP = 0.857; P < 0.001). Conclusion The new algorithm provides DBT volumes with better contrast and image quality, fewer artifacts, and improved visibility of calcifications for human observers, as well as improved detection performance with deep-learning algorithms.

Keywords: Digital breast tomosynthesis; deep learning; reconstruction algorithms; visual grading analysis.

Fig. 1.

In-house developed workstation for the scoring of the visual grading analysis reader study. The readers answered ten questions on a 5-point scale (1 = poor quality to 5 = excellent quality) and the lesions were outlined. The workstation automatically registered the results and provided a summary report per reader after each session.

Fig. 2.

Cumulative percentages of the scores (1 = poor quality, 5 = excellent quality) across readers for the four most relevant aspects that were found on average better for EMPIRE compared to FBP. (a) Absence of artifacts, (b) Image contrast, (c) Visibility calcifications, (d) Overall image quality.

Fig. 3.

Average scores per reader (1 = poor quality, 5 = excellent quality) for the four more relevant aspects that were found on average better for EMPIRE in comparison with FBP reconstruction. Differences between reconstruction algorithms for each reader were tested with the Mann–Whitney U (Wilcoxon) non-parametric test. (a) Absence of artefacts, (b) Image contrast, (c) Visibility calcifications, (d) Overall image quality.

Fig. 4.

Example ROIs of two DBT cases containing malignant calcifications (outlined) reconstructed with EMPIRE (left) and standard FBP (right). Three observers scored calcification visibility higher for EMPIRE in case (a), while all four of them scored EMPIRE higher in case (b). These images are displayed with the default window width and level set by the DBT system.

Fig. 5.

Example ROIs of a DBT case containing a malignant soft tissue lesion (outlined) reconstructed with EMPIRE (left) and standard FBP (right). Three observers scored soft tissue visibility similar between EMPIRE and FBP (one reader scored EMPIRE higher than FBP). Also note how an artefact nearby the nipple (white circle), due to a calcification in another DBT plane, is visible in FBP but not in EMPIRE. These images are displayed with the default window width and level set by the DBT system.

Fig. 6.

Example of patient DBT slice reconstructed with EMPIRE (left) and standard FBP (right). All four observers scored the artefacts on the FBP volume worse than on EMPIRE. It can be seen that for tissue near the skin line, EMPIRE provides a better visualization compared with FBP. Also, the large vein on the lateral side of the breast (under the star mark) shows more overshooting artefact (shadow like artefact, 21) in FBP than in EMPIRE. These images are displayed with the default window width and level set by the DBT system.

Fig. 7.

Complete (a) and partial (b) ROC curves of the same 3D-CNN trained and validated with EMPIRE images and trained and validated with FBP images, for the task of detecting suspicious calcifications in DBT slices.