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Back-Projection Filtration Image Reconstruction Approach for Reducing High-Density Object Artifacts in Digital Breast Tomosynthesis

H Kim*, S Lim , Y Lim , S Cho , KAIST, Daejeon, Korea


(Tuesday, 7/31/2018) 7:30 AM - 9:30 AM

Room: Room 202

Purpose: One of the dominant image artifacts in digital breast tomosynthesis (DBT) is high-density object artifacts in conjunction with a limited angle problem. We developed a very efficient method for reconstructing DBT images with much suppressed high-density object artifacts.

Methods: Because of limited angle scan in DBT, ripple artifacts may appear in the out-of-focus planes. The ripple artifacts occur more seriously with high-density objects. Moreover, undershoot artifacts that show up as dark fringes near the high-density object border are quite overwhelming in the reconstructed images by filtered back-projection (FBP) algorithm. To solve those problems, we developed a DBT image reconstruction scheme that is based on the back-projection filtration (BPF) algorithm. Data derivatives are back-projected with weighting factors to reduce ripple artifacts by use of a voting strategy. We generated another differentiated back-projection volume where edges of high-density objects are replaced by the background to reduce the undershoot artifacts. After applying the Hilbert transform, we blended the two images. For evaluation, we calculated artifacts volume fraction (AVF). We set the volume of interests (VOIs) that are contaminated by the artifacts, and segmented the artifacts volume. CIRS breast phantom and a lab-made pork phantom mimicking DBT-guided breast biopsy were scanned. We compared three image reconstruction
methods: conventional FBP algorithm, FBP utilizing a voting strategy, and the proposed method.

Results: Both ripple artifacts and undershoot artifacts were greatly suppressed by the proposed method. The proposed method resulted in AVF values about 80 % less than those in FBP reconstructions.

Conclusion: We have developed a BPF-based reconstruction algorithm to reduce high-density object artifacts in DBT and successfully demonstrated that both ripple artifacts and undershoot artifacts can be greatly suppressed. Our algorithm is believed to play an important role in many applications of digital tomosynthesis although we have focused on DBT only in this work.


Image Artifacts, Reconstruction, Breast


IM- Breast x-ray Imaging: Digital Breast Tomosynthesis (DBT)

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