Metal artifact reduction in 2D CT images with self-supervised cross-domain learning
نویسندگان
چکیده
The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation radiation therapy. In this work, we present a novel deep-learning-based approach for artifact reduction (MAR). order to alleviate need anatomically identical image pairs (i.e., artifact-corrupted and artifact-free image) network learning, propose self-supervised cross-domain learning framework. Specifically, train neural restore trace region values given metal-free sinogram, where is identified by forward projection masks. We then design FBP reconstruction loss encourage generate more perfect completion results residual-learning-based refinement module reduce secondary reconstructed images. To preserve fine structure details fidelity final MAR image, instead directly adopting CNN-refined images as output, incorporate replacement into our framework replace metal-affected projections original sinogram with prior generated CNN output. use filtered backward (FBP) algorithms reconstruction. conduct an extensive evaluation on simulated real data show effectiveness design. Our method produces superior outperforms other compelling methods. also demonstrate potential organ sites.
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ژورنال
عنوان ژورنال: Physics in Medicine and Biology
سال: 2021
ISSN: ['1361-6560', '0031-9155']
DOI: https://doi.org/10.1088/1361-6560/ac195c