An AI-based arterial input function model predicted myocardial blood flow with no significant difference compared to the reference dual-sequence method (2.79 vs 2.77 mL/min/g, P=0.33).
Observational (n=245)
Yes
Does an AI-based arterial input function (AI-AIF) accurately quantify myocardial blood flow in patients undergoing stress perfusion CMR compared to dual-sequence acquisition?
An AI-based deep learning model can accurately predict unsaturated arterial input function from standard CMR images, enabling quantitative stress perfusion CMR with a single-sequence acquisition.
Mean Difference: -0.11
Absolute Event Rate: 2.79% vs 2.77%
p-value: p=0.33
Abstract Aims One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training. Methods and results A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (n = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann–Whitney U test and Bland–Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF 2.77 mL/min/g (1.08) and predicted with the AI-AIF (2.79 mL/min/g (1.08), P = 0.33. Bland–Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of −0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95%) of myocardial segments. Conclusion Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.
Scannell et al. (Wed,) conducted a observational in Patients undergoing stress perfusion cardiac magnetic resonance (n=245). AI-based arterial input function (AI-AIF) vs. Reference dual-sequence acquisition AIFs (DS-AIFs) was evaluated on Fully-automated myocardial blood flow (MBF) (bias of -0.11 mL/min/g, p=0.33). An AI-based arterial input function model predicted myocardial blood flow with no significant difference compared to the reference dual-sequence method (2.79 vs 2.77 mL/min/g, P=0.33).