Fernández del Cerro, CarlosGalán González, AgustínGarcía Blas, Francisco JavierDesco Menéndez, ManuelAbella García, Mónica2023-06-052023-06-052022-06-12Cerro, 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.0277-786X1996-756X (online)https://hdl.handle.net/10016/37429Proceeding of: 7th International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), Baltimore, Maryland, 12-16 June 2022Tomosynthesis 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.8eng© 2022 SPIE.Chest tomosynthesisComputed tomographyDeep learningFDK-based reconstructionTransfer learningconference paperBiología y Biomedicinahttps://doi.org/10.1117/12.2646600open access1123042X8Proceedings of SPIE (7th International Conference on Image Formation in X-Ray Computed Tomography)12304CC/0000034353