iNav-based, Automated Coronary Magnetic Resonance Angiography for the Detection of Coronary Artery Stenosis (iNav-AUTO CMRA).

BACKGROUND: Coronary computed tomography angiography (CCTA) is recommended as the first line diagnostic imaging modality in low to intermediate risk individuals suspected of stable coronary artery disease (CAD). However, CCTA exposes patients to ionising radiation and potentially nephrotoxic contrast agents. Invasive coronary angiography (ICA) is the gold-standard investigation to guide coronary revascularisation strategy, however, invasive procedures incur an inherent risk to the patient. Coronary magnetic resonance angiography (Coronary MRA) avoids these issues. Nevertheless, clinical implementation is currently limited due to extended scanning durations, inconsistent image quality, and consequent lack of diagnostic accuracy. Several technical Coronary MRA innovations including advanced respiratory motion correction with 100% scan efficiency (no data rejection), fast image acquisition with motion-corrected undersampled image reconstruction and deep-learning (DL)-based automated planning have been implemented and now await clinical validation in multi-centre trials. METHODS: The objective of the iNav-AUTO CMRA prospective multi-centre study is to evaluate the diagnostic accuracy of a newly developed, state-of-the-art, standardised, and automated Coronary MRA framework compared to CCTA in 230 patients undergoing clinical investigation for CAD. The study protocol mandates the administration of oral beta-blockers to decrease heart rate to below 60bpm and the use of sublingual nitroglycerine spray to induce vasodilation. Additionally, the study incorporates the utilisation of standardised postprocessing with sliding-thin-slab multiplanar reformatting, in combination with evaluation of the source images, to optimize the visualisation of coronary artery stenosis. DISCUSSION: If proven effective, Coronary MRA could provide a non-invasive, needle-free, yet also clinically viable, alternative to CCTA. TRIAL REGISTRATION: This study is registered at clinicaltrials.gov (NCT05473117).

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

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.

Interscan reproducibility of computed tomography derived coronary plaque volume measurements.

BACKGROUND: Coronary computed tomography angiography (CCTA) enables detailed quantification and characterization of coronary atherosclerotic plaques, offering diagnostic and prognostic value. Interscan reproducibility studies on plaque volume measurements are limited. This study aims to assess the interscan reproducibility of coronary plaque quantification and the implications of clinical and technical characteristics on interscan reproducibility. METHODS: CCTA was performed twice in 101 patients with known coronary artery disease at a 1-h interval. The scans were conducted using identical CCTA acquisition protocols. Coronary plaque volumes were quantified using a semi-automated software and performed on a per-lesion, per-vessel, and per-patient level. RESULTS: Median plaque volumes were comparable between the first and second CCTA scan. Interscan correlation was high for total plaque (TP), non-calcified plaque (NCP), and calcified plaque (CP) across all analyses (Pearson's coefficient 0.93-0.99), but lower for low-density non-calcified plaque (LD-NCP) volume measurements (Pearson's coefficient 0.74-0.77). Bland-Altman analyses demonstrated higher interscan agreement on a per-patient level compared to on per-vessel and per-lesion level. Interscan reproducibility on CP volumes was affected by CT image quality with narrower LoA in scans with the highest image quality score (p â€‹= â€‹0.003), or lowest image reconstructive iteration level (p â€‹< â€‹0.001). Limits of agreement were significantly narrower for TP, NCP, and CP volumes in LAD-lesions and vessels compared to non-LAD lesions and vessels (p â€‹≤ â€‹0.001). CONCLUSION: Overall reproducibility of repeated CCTA derived plaque measurements by a semi-automated software was modest, and was influenced by image quality, image reconstruction settings, and lesion location.