Xie, Cheng
Zhang, Rory
Mensink, Sebastian
Gandharva, Rahul
Awni, Mustafa
Lim, Hester
Kachel, Stefan E.
Cheung, Ernest
Crawley, Richard
Churilov, Leonid
Bettencourt, Nuno
Chiribiri, Amedeo
Scannell, Cian M.
Lim, Ruth P. http://orcid.org/0000-0002-2842-5997
Funding for this research was provided by:
University of Melbourne
Article History
Received: 19 September 2023
Revised: 16 January 2024
Accepted: 18 January 2024
First Online: 10 February 2024
Declarations
:
: The scientific guarantor of this publication is Ruth P. Lim.
: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
: One of the authors has significant statistical expertise (Professor L Churilov).
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained.
: Some study subjects or cohorts have been previously reported in “CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images,” Eur Radiol 2022 Sep;32(9):5907-5920, in unrelated work. In the current study, 60/718 centre 1 subjects’, 268/268 centre 2 subjects’, 47/47 centre 3 subjects’ and 52/65 centre 4 subjects’ image data was used in the previous publication. However, note the very different purposes of the two studies, with the prior study’s purpose to purely sort different cardiac MRI sequences, and the current work focusing upon one single sequence and automated prediction of optimal inversion time to facilitate scanning workflow. The majority of subjects in this current study have not previously been reported.
: • retrospective• cross-sectional study• multicentre study