A Practical Truncation Correction Method for Digital Breast Tomosynthesis

Abstract

Digital breast tomosynthesis (DBT) mammography is a promising imaging technique for detecting early-stage breast cancers. However, DBT imaging usually contains truncation artifacts around the chest wall area, where many recurring breast tumors or masses are located. Extrapolation or interpolation and weighting techniques are used to suppress artifacts in existing methods, but these are inappropriate for DBT due to its high spatial resolution requirement. To solve this problem, we propose a practical truncation correction method, which can realize simultaneous truncation artifact reduction and image reconstruction (STARIR) for DBT. In contrast to extrapolation or interpolation estimations and weighting strategy, this integrated method renews each truncated voxel by considering the information of all measured projections rather than that of one projection at each reconstruction iteration. Qualitative and quantitative studies were performed on simulated and realistic DBT data to validate the proposed method. Results showed that STARIR reduces truncation artifacts and preserves tissue details in reconstructed images more effectively than do the current methods. The proposed method can be used for simultaneous and accurate truncation artifact reduction and image reconstruction in DBT.