10 research outputs found
Brief research report: Quantitative analysis of potential coronary microvascular disease in suspected long-COVID syndrome
BACKGROUND: Case 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. METHODS: Our 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. RESULTS: The 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). CONCLUSION: While 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
Aortic valve area using computed tomography‐derived correction factor to improve the validity of left ventricular outflow tract measurements
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
Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterised by a rapid and irregular electrical activation of the atria. Treatments for AF are often ineffective and few atrial biomarkers exist to automatically characterise atrial function and aid in treatment selection for AF. Clinical metrics of left atrial (LA) function, such as ejection fraction (EF) and active atrial contraction ejection fraction (aEF), are promising, but have until now typically relied on volume estimations extrapolated from single-slice images. In this work, we study volumetric functional biomarkers of the LA using a fully automatic SEGmentation of the left Atrium based on a convolutional neural Network (SEGANet). SEGANet was trained using a dedicated data augmentation scheme to segment the LA, across all cardiac phases, in short axis dynamic (CINE) Magnetic Resonance Images (MRI) acquired with full cardiac coverage. Using the automatic segmentations, we plotted volumetric time curves for the LA and estimated LA EF and aEF automatically. The proposed method yields high quality segmentations that compare well with manual segmentations (Dice scores [], median contour [] mm and Hausdorff distances [] mm). LA EF and aEF are also in agreement with literature values and are significantly higher in AF patients than in healthy volunteers. Our work opens up the possibility of automatically estimating LA volumes and functional biomarkers from multi-slice CINE MRI, bypassing the limitations of current single-slice methods and improving the characterisation of atrial function in AF patients
29 AI-AIF: artificial intelligence-based arterial input function correction for quantitative stress perfusion cardiac magnetic resonance
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