To improve digital breast tomosynthesis (DBT) image quality, we are developing model-based iterative reconstruction methods. We developed the SQS-DBCN algorithm, which incorporated detector blur into the system model and correlation into the noise model under some simplifying assumptions. In this paper, we further improved the regularization in the SQS-DBCN method by incorporating neighbors along the diagonal directions. To further understand the role of the different components in the system model of the SQS-DBCN method, we reconstructed DBT images without modeling either the detector blur or noise correlation for comparison. Visual comparison of the reconstructed images showed that regularizing with diagonal directions reduced artifacts and the noise level. The SQS-DBCN reconstructed images had better image quality than reconstructions without models for detector blur or correlated noise, as indicated by the contrast-to-noise ratios (CNR) of MCs and textural artifacts. These results indicated that regularized DBT reconstruction with detector blur and correlated noise modeling, even with simplifying assumptions, can improve DBT image quality compared to that without system modeling.
Jiabei Zheng, Jeffrey A. Fessler, and Heang-Ping Chan, "Effects of detector blur and correlated noise on digital breast tomosynthesis reconstruction," Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 1013226 (Presented at SPIE Medical Imaging: February 17, 2017; Published: 9 March 2017); https://doi.org/10.1117/12.2255689.
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