Novel dark-blood versus conventional bright-blood late gadolinium enhancement CMR: A pilot study comparing impact on myocardial ischaemic burden

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Background In patients with coronary artery disease (CAD), increasing myocardial ischaemic burden (MIB) is a strong predictor of adverse events. When measured by cardiovascular magnetic resonance (CMR), a MIB ≥12.5% is considered significant and often used as a threshold to guide revascularisation. Ischaemic scar can cause stress perfusion defects which do not represent ischaemia and should be excluded from the MIB calculation. Conventional bright-blood late gadolinium enhancement (LGE) is able to identify ischaemic scar but can suffer from poor scar-to-blood contrast, making accurate assessment of scar volume difficult. Dark-blood LGE methods increase scar-to-blood contrast and improve scar conspicuity which may impact the calculated scar burden and consequently the estimation of MIB when read in conjunction with perfusion images. Purpose To evaluate the impact of dark-blood LGE versus conventional bright-blood LGE on the estimation of MIB in patients with CAD. Methods 37 patients with suspected or known CAD who had evidence of CMR stress perfusion defects and ischaemic scar on LGE imaging were recruited. Patients underwent adenosine stress perfusion imaging followed by dark-blood LGE then conventional bright-blood LGE imaging at 3T. For dark-blood LGE, phase sensitive inversion recovery imaging with a shorter inversion time to null the LV blood-pool was used without any additional magnetization preparation. For each patient, three short-axis LGE slices were selected to match the three perfusion slice locations. Images were anonymised and analysed in random order. Ischaemic scar burden (ISB) was quantified for both LGE methods using a threshold >5 standard deviations above remote myocardium. Perfusion defect burden (PDB) was quantified by manual contouring of perfusion defects. MIB was calculated by subtracting the ISB from the PDB. Results MIB calculated using dark-blood LGE was 19% less compared to bright-blood LGE (15.7 ± 15.2% vs 19.4 ± 15.2%, p < 0.001). There was a strong positive correlation between the two LGE methods (rs = 0.960, p < 0.001, Figure 1A). Bland-Altman analysis revealed a significant fixed bias (mean bias = -3.6%, bias 95% CI: -2.6 to -4.7%, 95% limits of agreement: -9.8 to 2.5%) with no proportional bias (Figure 1B). MIB was calculated ≥12.5% and <12.5% by both LGE methods in 19 (51%) and 12 (32%) patients respectively. In 6 patients (16%), MIB was ≥12.5% using bright-blood LGE and <12.5% using dark-blood LGE (Figure 1A – orange data points). Overall, when used to classify MIB as <12.5% or ≥12.5%, there was only substantial agreement between the two LGE methods (κ=0.67, 95% CI: 0.45 to 0.90). Conclusions The use of dark-blood LGE in conjunction with perfusion imaging results in a lower estimate of MIB compared to conventional bright-blood LGE. This can cause disagreement around the threshold of clinically significant ischaemia which could impact clinical management in patients being considered for coronary revascularisation. Abstract Figure. Linear regression with corresponding B&A