Adverse cardiovascular magnetic resonance phenotypes are associated with greater likelihood of incident coronavirus disease 2019: findings from the UK Biobank.

BACKGROUND: Coronavirus disease 2019 (COVID-19) disproportionately affects older people. Observational studies suggest indolent cardiovascular involvement after recovery from acute COVID-19. However, these findings may reflect pre-existing cardiac phenotypes. AIMS: We tested the association of baseline cardiovascular magnetic resonance (CMR) phenotypes with incident COVID-19. METHODS: We studied UK Biobank participants with CMR imaging and COVID-19 testing. We considered left and right ventricular (LV, RV) volumes, ejection fractions, and stroke volumes, LV mass, LV strain, native T1, aortic distensibility, and arterial stiffness index. COVID-19 test results were obtained from Public Health England. Co-morbidities were ascertained from self-report and hospital episode statistics (HES). Critical care admission and death were from HES and death register records. We investigated the association of each cardiovascular measure with COVID-19 test result in multivariable logistic regression models adjusting for age, sex, ethnicity, deprivation, body mass index, smoking, diabetes, hypertension, high cholesterol, and prior myocardial infarction. RESULTS: We studied 310 participants (n = 70 positive). Median age was 63.8 [57.5, 72.1] years; 51.0% (n = 158) were male. 78.7% (n = 244) were tested in hospital, 3.5% (n = 11) required critical care admission, and 6.1% (n = 19) died. In fully adjusted models, smaller LV/RV end-diastolic volumes, smaller LV stroke volume, and poorer global longitudinal strain were associated with significantly higher odds of COVID-19 positivity. DISCUSSION: We demonstrate association of pre-existing adverse CMR phenotypes with greater odds of COVID-19 positivity independent of classical cardiovascular risk factors. CONCLUSIONS: Observational reports of cardiovascular involvement after COVID-19 may, at least partly, reflect pre-existing cardiac status rather than COVID-19 induced alterations

Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

“The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32245-8_83.”© 2019, Springer Nature Switzerland AG. Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing pipeline with robust automatic quality scoring, especially for clinical applications. We present a novel quality control-driven (QCD) framework to provide reliable segmentation using a set of different neural networks. In contrast to the prior segmentation and quality scoring methods, the proposed framework automatically selects the optimal segmentation on-the-fly from the multiple candidate segmentations available, directly utilizing the inherent Dice similarity coefficient (DSC) predictions. We trained and evaluated the framework on a large-scale cardiovascular magnetic resonance aortic cine image sequences from the UK Biobank Study. The framework achieved segmentation accuracy of mean DSC at 0.966, mean prediction error of DSC within 0.015, and mean error in estimating lumen area ≤17.6 mm2 for both ascending aorta and proximal descending aorta. This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies

Poor Bone Quality is Associated With Greater Arterial Stiffness: Insights From the UK Biobank

Osteoporosis and ischemic heart disease (IHD) represent important public health problems. Existing research suggests an association between the two conditions beyond that attributable to shared risk factors, with a potentially causal relationship. In this study, we tested the association of bone speed of sound (SOS) from quantitative heel ultrasound with (i) measures of arterial compliance from cardiovascular magnetic resonance (aortic distensibility [AD]); (ii) finger photoplethysmography (arterial stiffness index [ASI]); and (iii) incident myocardial infarction and IHD mortality in the UK Biobank cohort. We considered the potential mediating effect of a range of blood biomarkers and cardiometabolic morbidities and evaluated differential relationships by sex, menopause status, smoking, diabetes, and obesity. Furthermore, we considered whether associations with arterial compliance explained association of SOS with ischemic cardiovascular outcomes. Higher SOS was associated with lower arterial compliance by both ASI and AD for both men and women. The relationship was most consistent with ASI, likely relating to larger sample size available for this variable (n = 159,542 versus n = 18,229). There was no clear evidence of differential relationship by menopause, smoking, diabetes, or body mass index (BMI). Blood biomarkers appeared important in mediating the association for both men and women, but with different directions of effect and did not fully explain the observed effects. In fully adjusted models, higher SOS was associated with significantly lower IHD mortality in men, but less robustly in women. The association of SOS with ASI did not explain this observation. In conclusion, our findings support a positive association between bone and vascular health with consistent patterns of association in men and women. The underlying mechanisms are complex and appear to vary by sex

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

UK Biobank is a population-based cohort of half a million participants aged 40–69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world’s largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future direction

Quality assurance of quantitative cardiac T1-mapping in multicenter clinical trials - A T1 phantom program from the hypertrophic cardiomyopathy registry (HCMR) study.

BACKGROUND: Quantitative cardiovascular magnetic resonance T1-mapping is increasingly used for myocardial tissue characterization. However, the lack of standardization limits direct comparability between centers and wider roll-out for clinical use or trials. PURPOSE: To develop a quality assurance (QA) program assuring standardized T1 measurements for clinical use. METHODS: MR phantoms manufactured in 2013 were distributed, including ShMOLLI T1-mapping and reference T1 and T2 protocols. We first studied the T1 and T2 dependency on temperature and phantom aging using phantom datasets from a single site over 4 years. Based on this, we developed a multiparametric QA model, which was then applied to 78 scans from 28 other multi-national sites. RESULTS: T1 temperature sensitivity followed a second-order polynomial to baseline T1 values (R2 > 0.996). Some phantoms showed aging effects, where T1 drifted up to 49% over 40 months. The correlation model based on reference T1 and T2, developed on 1004 dedicated phantom scans, predicted ShMOLLI-T1 with high consistency (coefficient of variation 1.54%), and was robust to temperature variations and phantom aging. Using the 95% confidence interval of the correlation model residuals as the tolerance range, we analyzed 390 ShMOLLI T1-maps and confirmed accurate sequence deployment in 90%(70/78) of QA scans across 28 multiple centers, and categorized the rest with specific remedial actions. CONCLUSIONS: The proposed phantom QA for T1-mapping can assure correct method implementation and protocol adherence, and is robust to temperature variation and phantom aging. This QA program circumvents the need of frequent phantom replacements, and can be readily deployed in multicenter trials

Multi-Contrast MRI Registration of Carotid Arteries in Atherosclerotic and Normal Subjects

Clinical studies on atherosclerosis agree that multi-contrast MRI is the most promising technique for in-vivo characterization of carotid plaques. Multi-contrast image registration is essential for this application, because it corrects misalignments caused by patient motion during MRI acquisition. To date, it has not been determined which automatic method provides the best registration accuracy in carotid MRI. This study tries to answer this question by presenting an iterative coarse-to-fine algorithm that co-registers multi-contrast images of carotid arteries using three similarity metrics: Correlation Ratio (CR), Mutual Information (MI) and Gradient MI (GMI). The registration algorithm is first applied on the entire images and then only on the Region of Interest (ROI) of the carotid arteries using sub-pixel accuracy. The ROI is defined by an automatic carotid detection algorithm, which was tested on a group of 20 patients with different types of atherosclerotic plaques (sensitivity 91% and specificity 88%). Automatic registration was compared with image alignment obtained by manual operators (clinically qualified vascular specialists). Registration accuracies were measured using a novel MRI validation procedure, in which the gold standard is represented by in-plane rigid transformations applied by the MRI system to mimic neck movements. Overall, automatic methods (GMI = 181 ± 104 μm) produced lower registration errors than manual operators (365 ± 102 μm). GMI performed slightly better than CR and MI, suggesting that anatomical information improves registration accuracy in the carotid ROI. © 2010 Copyright SPIE - The International Society for Optical Engineering

Loss of fine structure and edge sharpness in fast-spin-echo carotid wall imaging: measurements and comparison with multiple-spin-echo in normal and atherosclerotic subjects.

PURPOSE: To test whether the k-space acquisition strategy used by fast-spin-echo (FSE) is a major source of blurring in carotid wall and plaque imaging, and investigate an alternative acquisition approach. MATERIALS AND METHODS: The effect of echo train length (ETL) and T(2) on the amount of blurring was studied in FSE simulations of vessel images. Edge sharpness was measured in black-blood T(1) W and proton density-weighted (PDW) carotid images acquired from 5 normal volunteers and 19 asymptomatic patients using both FSE and multiple-spin-echo (Multi-SE) sequences at 3 Tesla (T). Plaque images were classified and divided in group α (tissues' average T(2) ∼ 40-70 ms) and group β (plaque components with shorter T(2) ). RESULTS: Simulations predicted 26.9% reduction of vessel edge sharpness from Multi-SE to FSE images (ETL = 9, T(2) = 60 ms). This agreed with in vivo measurements in normal volunteers (27.4%) and in patient group α (26.2%), while in group β the loss was higher (31.6%). CONCLUSION: FSE significantly reduced vessel edge sharpness along the phase-encoding direction in T(1) W and PDW images. Blurring was stronger in the presence of plaque components with short T(2) times. This study shows a limitation of FSE and the potential of Multi-SE to improve the quality of carotid imaging