RT Conference Proceedings
T1 New reconstruction methodology for chest tomosynthesis based on deep learning
A1 Fernández del Cerro, Carlos
A1 Galán González, Agustín
A1 García Blas, Francisco Javier
A1 Desco Menéndez, Manuel
A1 Abella García, Mónica
AB 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.
PB International Society for Optics and Photonics
SN 0277-786X
SN 1996-756X  (online)
YR 2022
FD 2022-06-12
LK https://hdl.handle.net/10016/37429
UL https://hdl.handle.net/10016/37429
LA eng
NO Proceeding of: 7th International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), Baltimore, Maryland, 12-16 June 2022
NO This work has been supported by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación: PID2019-110369RB-I00/AEI/10.13039/501100011033 (RADHOR); PDC2021-121656-I00 (MULTIRAD), funded by MCIN/AEI/10.13039/501100011033 and by the European Union 'NextGenerationEU'/PRTR. Also funded by Comunidad de Madrid: Multiannual Agreement with UC3M in the line of 'Fostering Young Doctors Research' (DEEPCT-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation S2017/BMD-3867 RENIM-CM, co-funded by European Structural and Investment Fund. And also partially funded by CRUE Universidades, CSIC and Banco Santander (Fondo Supera Covid19), project RADCOV19 and by Instituto de Salud Carlos III through the project "PT20/00044", cofunded by European Regional Development Fund "A way to make Europe";. The CNIC is supported by Instituto de Salud Carlos III, Ministerio de Ciencia e Innovacióm and the Pro CNIC Foundation. The imaging and associated clinical data downloaded from MIDRC (The Medical Imaging Data Resource Center) and used for research in this publication was made possible by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under contracts 75N92020C00008 and 75N92020C00021.
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RD 30 jun. 2024