Martínez Sánchez, CristóbalFernandez Del Cerro, CarlosDesco Menéndez, ManuelAbella García, Mónica2023-05-242023-05-242021-07-19C. Martínez, C. F. Del Cerro, M. Desco & M. Abella (19-23 July 2021). New method for correcting beam-hardening artifacts in CT images via deep learning [poster]. Proceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, Leuven, Belgium, pp. 188-192.https://hdl.handle.net/10016/37354Proceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, 19-23 July 2021, Leuven, Belgium.Beam-hardening is the increase of the mean energy of an X-ray beam as it traverses a material. This effect produces two artifacts in the reconstructed image: cupping in homogeneous regions and dark bands among dense areas in heterogeneous regions. The correction methods proposed in the literature can be divided into post-processing and iterative methods. The former methods usually need a bone segmentation, which can fail in low-dose acquisitions, while the latter methods need several projections and reconstructions, increasing the computation time. In this work, we propose a new method for correcting the beamhardening artifacts in CT based on deep learning. A U-Net network was trained with rodent data for two scenarios: standard and low-dose. Results in an independent rodent study showed an optimum correction for both scenarios, similar to that of iterative approaches, but with a reduction of computational time of two orders of magnitude.5engAtribución-NoComercial-SinDerivadas 3.0 Españaconference posterBiología y BiomedicinaElectrónicaMedicinahttps://doi.org/10.48550/arXiv.2110.04143open access188192Proceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear MedicineCC/0000034354