Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography

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dc.contributor.authorWood, Gregory
dc.contributor.authorUglebjerg Pedersen, Alexandra
dc.contributor.authorKunze, Karl P
dc.contributor.authorNeji, Radhouene
dc.contributor.authorHajhosseiny, Reza
dc.contributor.authorWetzl, Jens
dc.contributor.authorYoon, Seung Su
dc.contributor.authorSchmidt, Michaela
dc.contributor.authorNorgaard, Bjarne Linde
dc.contributor.authorPrieto Vásquez, Claudia
dc.contributor.authorBotnar, René Michael
dc.contributor.authorKim, Won Yong
dc.date.accessioned2023-10-15T11:02:56Z
dc.date.available2023-10-15T11:02:56Z
dc.date.issued2023
dc.description.abstractBACKGROUND: Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection. METHODS: Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel. RESULTS: There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5-98.1 s). CONCLUSIONS: Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.
dc.fechaingreso.objetodigital2023-10-23
dc.fuente.origenSCOPUS
dc.identifier.doi10.1186/s12968-023-00962-9
dc.identifier.issn1532-429X
dc.identifier.scopusidSCOPUS_ID:85173003322
dc.identifier.urihttps://doi.org/10.1186/s12968-023-00962-9
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75155
dc.information.autorucEscuela de Ingeniería; Prieto Vásquez, Claudia; 0000-0003-4602-2523; 14195
dc.information.autorucInstituto de Ingeniería Biológica y Médica; Botnar, René Michael; 0000-0002-9447-4367; 1015313
dc.issue.numero52
dc.language.isoen
dc.pagina.final10
dc.pagina.inicio1
dc.publisherNLM (Medline)
dc.revistaJournal of cardiovascular magnetic resonance
dc.rightsacceso abierto
dc.subjectCardiac magnetic resonance angiography
dc.subjectCardiac rest period
dc.subjectDeep learning
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleAutomated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography
dc.typeartículo
dc.volumen25
sipa.codpersvinculados14195
sipa.codpersvinculados1015313
sipa.indexSCOPUS
sipa.trazabilidadSCOPUS;2023-10-15
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