File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Novel-view X-ray projection synthesis through geometry-integrated deep learning

TitleNovel-view X-ray projection synthesis through geometry-integrated deep learning
Authors
KeywordsGeometry-integrated deep learning
Projection view synthesis
X-ray imaging
Issue Date1-Apr-2022
PublisherElsevier
Citation
Medical Image Analysis, 2022, v. 77 How to Cite?
Abstract

X-ray imaging is a widely used approach to view the internal structure of a subject for clinical diagnosis, image-guided interventions and decision-making. The X-ray projections acquired at different view angles provide complementary information of patient's anatomy and are required for stereoscopic or volumetric imaging of the subject. In reality, obtaining multiple-view projections inevitably increases radiation dose and complicates clinical workflow. Here we investigate a strategy of obtaining the X-ray projection image at a novel view angle from a given projection image at a specific view angle to alleviate the need for actual projection measurement. Specifically, a Deep Learning-based Geometry-Integrated Projection Syn-thesis (DL-GIPS) framework is proposed for the generation of novel-view X-ray projections. The proposed deep learning model extracts geometry and texture features from a source-view projection, and then con-ducts geometry transformation on the geometry features to accommodate the change of view angle. At the final stage, the X-ray projection in the target view is synthesized from the transformed geometry and the shared texture features via an image generator. The feasibility and potential impact of the proposed DL-GIPS model are demonstrated using lung imaging cases. The proposed strategy can be generalized to a general case of multiple projections synthesis from multiple input views and potentially provides a new paradigm for various stereoscopic and volumetric imaging with substantially reduced efforts in data acquisition.


Persistent Identifierhttp://hdl.handle.net/10722/331869
ISSN
2023 Impact Factor: 10.7
2023 SCImago Journal Rankings: 4.112
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShen, L-
dc.contributor.authorYu, L-
dc.contributor.authorZhao, W-
dc.contributor.authorPauly, J-
dc.contributor.authorXing, L-
dc.date.accessioned2023-09-28T04:59:14Z-
dc.date.available2023-09-28T04:59:14Z-
dc.date.issued2022-04-01-
dc.identifier.citationMedical Image Analysis, 2022, v. 77-
dc.identifier.issn1361-8415-
dc.identifier.urihttp://hdl.handle.net/10722/331869-
dc.description.abstract<p>X-ray imaging is a widely used approach to view the internal structure of a subject for clinical diagnosis, image-guided interventions and decision-making. The X-ray projections acquired at different view angles provide complementary information of patient's anatomy and are required for stereoscopic or volumetric imaging of the subject. In reality, obtaining multiple-view projections inevitably increases radiation dose and complicates clinical workflow. Here we investigate a strategy of obtaining the X-ray projection image at a novel view angle from a given projection image at a specific view angle to alleviate the need for actual projection measurement. Specifically, a Deep Learning-based Geometry-Integrated Projection Syn-thesis (DL-GIPS) framework is proposed for the generation of novel-view X-ray projections. The proposed deep learning model extracts geometry and texture features from a source-view projection, and then con-ducts geometry transformation on the geometry features to accommodate the change of view angle. At the final stage, the X-ray projection in the target view is synthesized from the transformed geometry and the shared texture features via an image generator. The feasibility and potential impact of the proposed DL-GIPS model are demonstrated using lung imaging cases. The proposed strategy can be generalized to a general case of multiple projections synthesis from multiple input views and potentially provides a new paradigm for various stereoscopic and volumetric imaging with substantially reduced efforts in data acquisition.<br></p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofMedical Image Analysis-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectGeometry-integrated deep learning-
dc.subjectProjection view synthesis-
dc.subjectX-ray imaging-
dc.titleNovel-view X-ray projection synthesis through geometry-integrated deep learning-
dc.typeArticle-
dc.identifier.doi10.1016/j.media.2022.102372-
dc.identifier.scopuseid_2-s2.0-85124000583-
dc.identifier.volume77-
dc.identifier.isiWOS:000793644300005-
dc.identifier.issnl1361-8415-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats