Aortic valve area is one of the main criteria used by echocardiography to determine the degree of valvular aortic stenosis, and it is calculated using the continuity equation which assumes that the flow volume of blood is equal at two points, the aortic valve area and the left ventricular outflow tract (LVOT). The main fallacy of this equation is the assumption that the LVOT area which is used to calculate the flow volume at the LVOT level is circular, where it is often an ellipse and sometimes irregular. The aim of this review is to explain the physiology of the continuity equation, the different sources of errors, the added benefits of using three-dimensional imaging modalities to measure LVOT area, the latest recommendations related to valvular aortic stenosis, and to introduce future perspectives. A literature review of studies comparing aortic valve area and LVOT area, after using three-dimensional data, has shown underestimation of both measurements when using the continuity equation. This has more impact on patients with discordant echocardiographic measurements when aortic valve area is disproportionate to haemodynamic measurements in assessing the degree of aortic stenosis. Although fusion imaging modalities of LVOT area can help in certain group of patients to address the issue of aortic valve area underestimation, further research on introducing a correction factor to the conventional continuity equation might be more rewarding, saving patients additional tests and potential radiation, with no clear evidence of cost-effectiveness.
BackgroundCase series have reported persistent cardiopulmonary symptoms, often termed long-COVID or post-COVID syndrome, in more than half of patients recovering from Coronavirus Disease 19 (COVID-19). Recently, alterations in microvascular perfusion have been proposed as a possible pathomechanism in long-COVID syndrome. We examined whether microvascular perfusion, measured by quantitative stress perfusion cardiac magnetic resonance (CMR), is impaired in patients with persistent cardiac symptoms post-COVID-19.MethodsOur population consisted of 33 patients post-COVID-19 examined in Berlin and London, 11 (33%) of which complained of persistent chest pain and 13 (39%) of dyspnea. The scan protocol included standard cardiac imaging and dual-sequence quantitative stress perfusion. Standard parameters were compared to 17 healthy controls from our institution. Quantitative perfusion was compared to published values of healthy controls.ResultsThe stress myocardial blood flow (MBF) was significantly lower [31.8 ± 5.1 vs. 37.8 ± 6.0 (μl/g/beat), P < 0.001] and the T2 relaxation time was significantly higher (46.2 ± 3.6 vs. 42.7 ± 2.8 ms, P = 0.002) post-COVID-19 compared to healthy controls. Stress MBF and T1 and T2 relaxation times were not correlated to the COVID-19 severity (Spearman r = −0.302, −0.070, and −0.297, respectively) or the presence of symptoms. The stress MBF showed a U-shaped relation to time from PCR to CMR, no correlation to T1 relaxation time, and a negative correlation to T2 relaxation time (Pearson r = −0.446, P = 0.029).ConclusionWhile we found a significantly reduced microvascular perfusion post-COVID-19 compared to healthy controls, this reduction was not related to symptoms or COVID-19 severity.
Aims Given the inherent inaccuracies stemming from the assumption that the left ventricular outflow tract (LVOT) is circular, this study aimed to improve the accuracy of transthoracic echocardiography (TTE)‐based aortic valve area (AVA) calculation using continuity equation (CE) by introducing a correction factor (CF) derived from multidetector computed tomography angiography (MDCTA) images and validate it in aortic stenosis (AS) patients. Methods and Results This retrospective study used MDCTA images of 400 patients for modeling and 403 TTE dataset for validation. Echocardiographic parasternal long‐axis view was modeled using MDCTA, and LVOT diameter (D1) was measured. Direct planimetry of LVOT area was performed and subsequently converted into a theoretical circle. The assumed circle (D2) diameter was derived, and D2/D1 was calculated and termed as the CF. The CF was 1.13, and it improved the agreement between MDCTA‐ and TTE‐derived LVOT areas and correlation between AVA and peak velocity, mean pressure gradient, and velocity ratio. In discordant subgroups of severe AS, the CF reclassified patients to moderate AS in 40% in the low flow (LF), low gradient (LG), and low ejection fraction (EF) group; 53% in the LF, LG, and normal EF group; and 68% in the LF, high gradient, and normal EF group. Conclusions CF of 1.13 derived from MDCTA improved the accuracy of TTE‐derived LVOT area and AVA and improved correlation with hemodynamic variables in AS patients. Reclassification of AS patients using CF may have clinical applicability for patient selection for early intervention.
Purpose 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 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 was 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. Results 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.
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