2024
DOI: 10.3390/jcdd11100304
|View full text |Cite
|
Sign up to set email alerts
|

Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction

Tsubasa Morioka,
Shingo Kato,
Ayano Onoma
et al.

Abstract: Background: The utility of synthetic ECV, which does not require hematocrit values, has been reported; however, high-quality CT images are essential for accurate quantification. Second-generation Deep Learning Reconstruction (DLR) enables low-noise and high-resolution cardiac CT images. The aim of this study is to compare the differences among four reconstruction methods (hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and second-generation DLR) in the quantification of… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 24 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?