File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images

TitleDeep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Authors
Keywordsprior image
Metal artifact reduction
residual learning
sinogram completion
deep learning
Issue Date2021
Citation
IEEE Transactions on Medical Imaging, 2021, v. 40, n. 1, p. 228-238 How to Cite?
AbstractComputed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.
Persistent Identifierhttp://hdl.handle.net/10722/299485
ISSN
2023 Impact Factor: 8.9
2023 SCImago Journal Rankings: 3.703
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, Lequan-
dc.contributor.authorZhang, Zhicheng-
dc.contributor.authorLi, Xiaomeng-
dc.contributor.authorXing, Lei-
dc.date.accessioned2021-05-21T03:34:30Z-
dc.date.available2021-05-21T03:34:30Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Medical Imaging, 2021, v. 40, n. 1, p. 228-238-
dc.identifier.issn0278-0062-
dc.identifier.urihttp://hdl.handle.net/10722/299485-
dc.description.abstractComputed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Medical Imaging-
dc.subjectprior image-
dc.subjectMetal artifact reduction-
dc.subjectresidual learning-
dc.subjectsinogram completion-
dc.subjectdeep learning-
dc.titleDeep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TMI.2020.3025064-
dc.identifier.pmid32956044-
dc.identifier.pmcidPMC7875504-
dc.identifier.scopuseid_2-s2.0-85098848372-
dc.identifier.volume40-
dc.identifier.issue1-
dc.identifier.spage228-
dc.identifier.epage238-
dc.identifier.eissn1558-254X-
dc.identifier.isiWOS:000604883800020-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats