Abstract

Purpose:Digital breast tomosynthesis (DBT) is an emerging breast imaging modality. It uses x‐ray absorption projection images acquired over a narrow angular span to provide high spatial resolution pseudo‐three dimensional images for breast cancer screening and diagnosis. Unfortunately, the narrow range used to acquire data (typically <30 degrees) results in significant out of plane artifacts for high contrast objects when conventional reconstruction techniques are used. In this work, the denoised ordered‐subset statistically penalized algebraic reconstruction technique (DOS‐SPART) algorithm was adapted to breast tomosynthesis imaging to reduce out‐of‐plane blurring artifacts.Methods:The DOS‐SPART algorithm was implemented for use with DBT datasets. The ACR mammography accreditation phantom was imaged for quantitative measurement and subjective image analysis. The artifact spread function (ASF) was measured for images generated by the commercial reconstruction method as well as DOS‐SPART with different total number of iterations (5, 10, 15, and 20). The FWHM of the ASF was used to quantify out‐of‐plane blurring. A cluster of calcifications in the ACR phantom was also inspected subjectively, and images above and below the feature were examined for artifacts.Results:The DOS‐SPART algorithm was able to efficiently reconstruct sharp image at the in‐focus plane, while significantly reducing out‐of‐plane blurring artifacts (total reconstruction time <90 seconds for a 1996×2457×70 voxel volume with 5 iterations). The FWHM of the ASF was reduced by as much as 35% using the algorithm. Subjectively, the out‐of‐plane artifacts from the high contrast calcification are still clearly visible in the commercial reconstruction at 1 cm above or below the in‐focus plane, whereas no artifacts from the calcification are detectable in the out‐of‐plane images generated with DOS‐SPART.Conclusion:The application of DOS‐SPART to DBT can help limit out‐of‐plane artifacts in DBT imaging, potentially improving localization of microcalcifications and image contrast for adjacent structures.Funding support: This work was support in part by NIH R01 EB020521. Disclosures: J Garrett: None. Y. Li: None. K. Li: None. GH Chen: Research funded, GE Healthcare and Siemens AX

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