AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance

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

Journal of Cardiovascular Magnetic Resonance BioMed Central Oral presentation

An MRI examination for evaluation of aortic dissection usin

High-resolution magnetic resonance myocardial perfusion imaging at 3.0-Tesla to detect hemodynamically significant coronary stenoses as determined by fractional flow reserve

OBJECTIVES: The objective of this study was to compare visual and quantitative analysis of high spatial resolution cardiac magnetic resonance (CMR) perfusion at 3.0-T against invasively determined fractional flow reserve (FFR). BACKGROUND: High spatial resolution CMR myocardial perfusion imaging for the detection of coronary artery disease (CAD) has recently been proposed but requires further clinical validation. METHODS: Forty-two patients (33 men, age 57.4 ± 9.6 years) with known or suspected CAD underwent rest and adenosine-stress k-space and time sensitivity encoding accelerated perfusion CMR at 3.0-T achieving in-plane spatial resolution of 1.2 × 1.2 mm(2). The FFR was measured in all vessels with >50% severity stenosis. Fractional flow reserve <0.75 was considered hemodynamically significant. Two blinded observers visually interpreted the CMR data. Separately, myocardial perfusion reserve (MPR) was estimated using Fermi-constrained deconvolution. RESULTS: Of 126 coronary vessels, 52 underwent pressure wire assessment. Of these, 27 lesions had an FFR <0.75. Sensitivity and specificity of visual CMR analysis to detect stenoses at a threshold of FFR <0.75 were 0.82 and 0.94 (p < 0.0001), respectively, with an area under the receiver-operator characteristic curve of 0.92 (p < 0.0001). From quantitative analysis, the optimum MPR to detect such lesions was 1.58, with a sensitivity of 0.80, specificity of 0.89 (p < 0.0001), and area under the curve of 0.89 (p < 0.0001). CONCLUSIONS: High-resolution CMR MPR at 3.0-T can be used to detect flow-limiting CAD as defined by FFR, using both visual and quantitative analyses

euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling

The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling