Abstract
Cancer screening guidelines recommend annual screening with low-dose Computed Tomography (CT) for high-risk groups to reduce lung cancer mortality. Unfortunately, lung CT effectiveness can be strongly impacted by the considered reconstruction kernel. This selection is (almost) final, implying that it is no longer possible to change the used reconstruction kernel once applied, unless a sinogram for the conversion is available. The aim of this paper was to introduce a new sinogram-free kernel conversion in the contest of lung CT imaging. In particular, we wanted to define a procedure able to deal with different acquisition protocols, able to be used in an unpaired images scenario. To this aim, we leveraged a CycleGAN, considering the CT kernel conversion task as a style transfer problem. Results show that the CT kernel conversion can be effectively addressed as a style transfer problem.
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Acknowledgments
The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research, as well as the availability of the Calculation Centre SCoPE of the University of Naples Federico II and its staff. This work is part of the “Synergy-net: Research and Digital Solutions against Cancer” project (funded in the framework of the POR Campania FESR 2014-2020 - CUP B61C17000090007).
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Gravina, M. et al. (2022). Leveraging CycleGAN in Lung CT Sinogram-free Kernel Conversion. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_9
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DOI: https://doi.org/10.1007/978-3-031-06427-2_9
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