Browsing by Author "Wood, Gregory"

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    Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography
    (NLM (Medline), 2023) Wood, Gregory; Uglebjerg Pedersen, Alexandra; Kunze, Karl P; Neji, Radhouene; Hajhosseiny, Reza; Wetzl, Jens; Yoon, Seung Su; Schmidt, Michaela; Norgaard, Bjarne Linde; Prieto Vásquez, Claudia; Botnar, René Michael; Kim, Won Yong
    BACKGROUND: 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.
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    High-resolution automated free-breathing coronary magnetic resonance angiography in comparison with coronary computed tomography angiography
    (2025) Wood, Gregory; Uglebjerg Pedersen, Alexandra; Linde Nørgaard, Bjarne; Alcaraz Frederiksen, Christian; Møller Jensen, Jesper; Kunze, Karl-Philipp; Neji, Radhouene; Wetzl, Jens; Prieto Vásquez, Claudia Del Carmen; Botnar, Rene Michael; Yong Kim, Won
    Aims Clinical implementation of coronary magnetic resonance angiography (CMRA) is limited due to variability in image quality. A protocol utilizing an image navigator (iNAV) integrated with automated scan planning has been developed to facilitate consistent diagnostic image quality. The aim of this study was to evaluate the agreement of automated iNAV CMRA compared with coronary computed tomography angiography (CCTA) using Coronary Artery Disease-Reporting and Data System (CAD-RADS) to classify coronary artery disease (CAD). Methods and results Ninety-five individuals underwent automated iNAV CMRA at a resolution of 0.7 mm3 with a deep learning–assisted automated scan planning and trigger-delay detection protocol. CMRA and CCTA data sets were analysed using CAD-RADS to classify the per-patient severity of CAD. Additionally, the accuracy of both imaging modalities in predicting referral for invasive coronary angiography (ICA) and coronary revascularization was assessed. CMRA classification for CAD-RADS ≥ 1, ≥2, ≥3, and ≥4 agreed with CCTA for 80%, 73%, 63%, and 70% of cases, respectively. The area under the receiver operating characteristic curves with CAD-RADS ≥ 4 and ≥3 for CMRA and CCTA were comparable in predicting ICA referral (0.75 vs. 0.70, P = 0.687, and 0.70 vs. 0.70, P = 0.945) and revascularization (0.72 vs. 0.74, P = 0.811, and 0.68 vs. 0.76, P = 0.089). Conclusion A novel automated iNAV CMRA protocol was implemented, investigating individuals at risk of CAD. Using the CAD-RADS classification, there was moderate to good agreement between CMRA and CCTA. In patients with CAD-RADS ≥ 4 and ≥3, CMRA was as effective as CCTA in predicting ICA referral and revascularization.