Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography an...
Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Objectives
Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition...
Alternative Titles
Full title
Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2638854745
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2638854745
Other Identifiers
ISSN
0938-7994,1432-1084
E-ISSN
1432-1084
DOI
10.1007/s00330-021-08367-x
