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Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic
Gregory Wood1,2, Alexandra Uglebjerg Pedersen3,4, Karl P Kunze5,6
1Department of Cardiology, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200, Aarhus N, Denmark. gregory.wood@clin.au.dk.
Insights
Deep learning software automates trigger delay detection for coronary MRA, improving workflow efficiency. This method is faster than manual operator assessment, enhancing reproducibility in coronary artery disease imaging.
Area of Science:
- Cardiovascular Imaging
- Medical Imaging Analysis
- Artificial Intelligence in Medicine
Background:
- Coronary magnetic resonance angiography (coronary MRA) is a viable tool for diagnosing coronary artery disease (CAD).
- Accurate trigger delay determination is crucial for minimizing cardiac motion artifacts in coronary MRA.
- Current operator-dependent methods for trigger delay assessment can lead to inconsistent scan acquisition.
Purpose of the Study:
- To evaluate the efficacy of deep learning (DL) software in automating cardiac rest period detection for coronary MRA.
- To compare the performance of DL-based trigger delay determination against operator-led and formula-based methods.
- To assess the impact of different trigger delay strategies on coronary MRA image quality and acquisition time.
Main Methods:
- Thirty participants underwent motion-corrected coronary MRA using an iNAV-based sequence.
- Trigger delay was determined using three methods: DL software, experienced operator, and a pre-existing formula.
- Image quality was assessed by visible coronary vessel length and sharpness; acquisition times were recorded.
Main Results:
- No significant differences in image quality (vessel length, sharpness) were observed across the three trigger delay determination methods.
- The DL software method was significantly faster (106 ± 38.0 s) than the operator-led approach (168 ± 39.2 s) (p < 0.01).
- The formula method showed slightly greater left circumflex vessel length compared to other methods.
Conclusions:
- Deep learning software effectively automates optimal trigger delay detection for coronary MRA.
- This automated approach can simplify workflow and enhance the reproducibility of coronary MRA acquisition.
- DL-based trigger delay determination offers an efficient alternative to manual methods in CAD imaging.
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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