Publication: New method for correcting beam-hardening artifacts in CT images via deep learning
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2021-07-19
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Cornell University
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Abstract
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.
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Proceedings of the 16th Virtual International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, 19-23 July 2021, Leuven, Belgium.
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C. 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.