Reducing high-density object artifacts with iterative image reconstruction in digital tomosynthesis

Hyeongseok Kim; Jongha Lee; Seungryong Cho

doi:10.1117/12.2534892

28 May 2019 Reducing high-density object artifacts with iterative image reconstruction in digital tomosynthesis

Hyeongseok Kim, Jongha Lee, Seungryong Cho

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110722Q (2019) https://doi.org/10.1117/12.2534892
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

In digital tomosynthesis, high-density object artifacts such as ripples and undershoots can show up in the reconstructed image in conjunction with a limited angle problem and may hinder an accurate diagnosis. In this study, we propose an iterative image reconstruction method for reducing such artifacts by use of a voting strategy with a data fidelity term that involves derivative data. It has been confirmed that the voting strategy can help reduce high-density object artifacts in the algebraic iterative reconstruction framework for tomosyntheis and more importantly shown that its contribution greatly improves when the derivative data term is jointly used in the cost function. For evaluation, the CIRS breast phantom and a forearm phantom with metal implants were scanned using a prototype digital breast tomosynthesis system and a chest digital tomosynthesis system, respectively.

Citation Download Citation

Hyeongseok Kim, Jongha Lee, and Seungryong Cho "Reducing high-density object artifacts with iterative image reconstruction in digital tomosynthesis", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110722Q (28 May 2019); https://doi.org/10.1117/12.2534892

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