Publication: New reconstruction methodology for chest tomosynthesis based on deep learning
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2022-06-12
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International Society for Optics and Photonics
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Abstract
Tomosynthesis offers an alternative to planar radiography providing pseudo-tomographic information at a much lower radiation dose than CT. The fact that it cannot convey information about the density poses a major limitation towards the use of tomosynthesis in chest imaging, due to the wide range of pathologies that present an increase in the density of the pulmonary parenchyma. Previous works have attempted to improve image quality through enhanced analytical, iterative algorithms, or including a deep learning-based step in the reconstruction, but the results shown are still far from the quantitative information of a CT. In this work, we propose a reconstruction methodology consisting of a filtered back-projection step followed by post-processing based on Deep Learning to obtain a tomographic image closer to CT. Preliminary results show the potential of the proposed methodology to obtain true tomographic information from tomosynthesis data, which could replace CT scans in applications where the radiation dose is critical.
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Proceeding of: 7th International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), Baltimore, Maryland, 12-16 June 2022
Keywords
Chest tomosynthesis, Computed tomography, Deep learning, FDK-based reconstruction, Transfer learning
Bibliographic citation
Cerro, Carlos F. del, et al. New reconstruction methodology for chest tomosynthesis based on deep learning. In: Proceedings of SPIE, vol. (7th International Conference on Image Formation in X-Ray Computed Tomography), 123042X. SPIE, June 2022, 8 p.