Cardiac Remodeling After Hypertensive Pregnancy Following Physician-Optimized Blood Pressure Self-Management: The POP-HT Randomized Clinical Trial Imaging Substudy.

BACKGROUND: Hypertensive pregnancy disorders are associated with adverse cardiac remodeling, which can fail to reverse in the postpartum period in some women. The Physician-Optimized Postpartum Hypertension Treatment trial demonstrated that improved blood pressure control while the cardiovascular system recovers postpartum associates with persistently reduced blood pressure. We now report the effect on cardiac remodeling. METHODS: In this prospective, randomized, open-label, blinded end point trial, in a single UK hospital, 220 women were randomly assigned 1:1 to self-monitoring with research physician-optimized antihypertensive titration or usual postnatal care from a primary care physician and midwife. Participants were 18 years of age or older, with preeclampsia or gestational hypertension, requiring antihypertensives on hospital discharge postnatally. Prespecified secondary cardiac imaging outcomes were recorded by echocardiography around delivery, and again at blood pressure primary outcome assessment, around 9 months postpartum, when cardiovascular magnetic resonance was also performed. RESULTS: A total of 187 women (101 intervention; 86 usual care) underwent echocardiography at baseline and follow-up, at a mean 258±14.6 days postpartum, of which 174 (93 intervention; 81 usual care) also had cardiovascular magnetic resonance at follow-up. Relative wall thickness by echocardiography was 0.06 (95% CI, 0.07-0.05; P<0.001) lower in the intervention group between baseline and follow-up, and cardiovascular magnetic resonance at follow-up demonstrated a lower left ventricular mass (-6.37 g/m2; 95% CI, -7.99 to -4.74; P<0.001), end-diastolic volume (-3.87 mL/m2; 95% CI, -6.77 to -0.98; P=0.009), and end-systolic volume (-3.25 mL/m2; 95% CI, 4.87 to -1.63; P<0.001) and higher left and right ventricular ejection fraction by 2.6% (95% CI, 1.3-3.9; P<0.001) and 2.8% (95% CI, 1.4-4.1; P<0.001), respectively. Echocardiography-assessed left ventricular diastolic function demonstrated a mean difference in average E/E' of 0.52 (95% CI, -0.97 to -0.07; P=0.024) and a reduction in left atrial volumes of -4.33 mL/m2 (95% CI, -5.52 to -3.21; P<0.001) between baseline and follow-up when adjusted for baseline differences in measures. CONCLUSIONS: Short-term postnatal optimization of blood pressure control after hypertensive pregnancy, through self-monitoring and physician-guided antihypertensive titration, associates with long-term changes in cardiovascular structure and function, in a pattern associated with more favorable cardiovascular outcomes. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04273854.

Myocardial Involvement After Hospitalization for COVID-19 Complicated by Troponin Elevation: A Prospective, Multicenter, Observational Study.

BACKGROUND: Acute myocardial injury in hospitalized patients with coronavirus disease 2019 (COVID-19) has a poor prognosis. Its associations and pathogenesis are unclear. Our aim was to assess the presence, nature, and extent of myocardial damage in hospitalized patients with troponin elevation. METHODS: Across 25 hospitals in the United Kingdom, 342 patients with COVID-19 and an elevated troponin level (COVID+/troponin+) were enrolled between June 2020 and March 2021 and had a magnetic resonance imaging scan within 28 days of discharge. Two prospective control groups were recruited, comprising 64 patients with COVID-19 and normal troponin levels (COVID+/troponin-) and 113 patients without COVID-19 or elevated troponin level matched by age and cardiovascular comorbidities (COVID-/comorbidity+). Regression modeling was performed to identify predictors of major adverse cardiovascular events at 12 months. RESULTS: Of the 519 included patients, 356 (69%) were men, with a median (interquartile range) age of 61.0 years (53.8, 68.8). The frequency of any heart abnormality, defined as left or right ventricular impairment, scar, or pericardial disease, was 2-fold greater in cases (61% [207/342]) compared with controls (36% [COVID+/troponin-] versus 31% [COVID-/comorbidity+]; P<0.001 for both). More cases than controls had ventricular impairment (17.2% versus 3.1% and 7.1%) or scar (42% versus 7% and 23%; P<0.001 for both). The myocardial injury pattern was different, with cases more likely than controls to have infarction (13% versus 2% and 7%; P<0.01) or microinfarction (9% versus 0% and 1%; P<0.001), but there was no difference in nonischemic scar (13% versus 5% and 14%; P=0.10). Using the Lake Louise magnetic resonance imaging criteria, the prevalence of probable recent myocarditis was 6.7% (23/342) in cases compared with 1.7% (2/113) in controls without COVID-19 (P=0.045). During follow-up, 4 patients died and 34 experienced a subsequent major adverse cardiovascular event (10.2%), which was similar to controls (6.1%; P=0.70). Myocardial scar, but not previous COVID-19 infection or troponin, was an independent predictor of major adverse cardiovascular events (odds ratio, 2.25 [95% CI, 1.12-4.57]; P=0.02). CONCLUSIONS: Compared with contemporary controls, patients with COVID-19 and elevated cardiac troponin level have more ventricular impairment and myocardial scar in early convalescence. However, the proportion with myocarditis was low and scar pathogenesis was diverse, including a newly described pattern of microinfarction. REGISTRATION: URL: https://www.isrctn.com; Unique identifier: 58667920.

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.

BACKGROUND: Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems. METHODS: Virtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull's-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (R), and intraclass correlation coefficients. Proof-of-principle histopathologic comparison of VNE in a porcine model of myocardial infarction also was performed. RESULTS: VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all P<0.001). VNE correlated strongly with LGE in quantifying scar size (R, 0.89; intraclass correlation coefficient, 0.94) and transmurality (R, 0.84; intraclass correlation coefficient, 0.90) in 66 patients (277 test data sets). Two cardiovascular magnetic resonance experts reviewed all test image slices and reported an overall accuracy of 84% for VNE in detecting scars when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction. CONCLUSIONS: VNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence-based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future.

Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to develop a contrast agent-free technology to replace LGE for faster and cheaper CMR scans. METHODS: A CMR virtual native enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1 maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multicenter Hypertrophic Cardiomyopathy Registry, using hypertrophic cardiomyopathy as an exemplar. The datasets were randomized into 2 independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement, and myocardial lesion burden quantification. Image quality was compared using a nonparametric Wilcoxon test. Intra- and interobserver agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC. RESULTS: A total of 1348 hypertrophic cardiomyopathy patients provided 4093 triplets of matched T1 maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development and 345 were used for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets; P<0.001 [Wilcoxon test]). VNE revealed lesions characteristic of hypertrophic cardiomyopathy in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyperintensity myocardial lesions (r=0.77-0.79; ICC=0.77-0.87; P<0.001) and intermediate-intensity lesions (r=0.70-0.76; ICC=0.82-0.85; P<0.001). The native CMR images (cine plus T1 map) required for VNE can be acquired within 15 minutes and producing a VNE image takes less than 1 second. CONCLUSIONS: VNE is a new CMR technology that resembles conventional LGE but without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

Subclinical Changes in Cardiac Functional Parameters as Determined by Cardiovascular Magnetic Resonance (CMR) Imaging in Sleep Apnea and Snoring: Findings from UK Biobank.

Background and Objectives: Obstructive sleep apnea (OSA) is a common disorder with an increased risk for left ventricular and right ventricular dysfunction. Most studies to date have examined populations with manifest cardiovascular disease using echocardiography to analyze ventricular dysfunction with little or no reference to ventricular volumes or myocardial mass. Our aim was to explore these parameters with cardiac MRI. We hypothesized that there would be stepwise increase in left ventricular mass and right ventricular volumes from the unaffected, to the snoring and the OSA group. Materials and Methods: We analyzed cardiac MRI data from 4978 UK Biobank participants free from cardiovascular disease. Participants were allocated into three cohorts: with OSA, with self-reported snoring and without OSA or snoring (n = 118, 1886 and 2477). We analyzed cardiac parameters from balanced cine-SSFP sequences and indexed them to body surface area. Results: Patients with OSA were mostly males (47.3% vs. 79.7%; p < 0.001) with higher body mass index (25.7 ± 4.0 vs. 31.3 ± 5.3 kg/m²; p < 0.001) and higher blood pressure (135 ± 18 vs. 140 ± 17 mmHg; p = 0.012) compared to individuals without OSA or snoring. Regression analysis showed a significant effect for OSA in left ventricular end-diastolic index (LVEDVI) (ß = -4.9 ± 2.4 mL/m²; p = 0.040) and right ventricular end-diastolic index (RVEDVI) (ß = -6.2 ± 2.6 mL/m²; p = 0.016) in females and for right ventricular ejection fraction (RVEF) (ß = 1.7 ± 0.8%; p = 0.031) in males. A significant effect was discovered in snoring females for left ventricular mass index (LVMI) (ß = 3.5 ± 0.9 g/m²; p < 0.001) and in males for left ventricular ejection fraction (LVEF) (ß = 1.0 ± 0.3%; p = 0.001) and RVEF (ß = 1.2 ± 0.3%; p < 0.001). Conclusion: Our study suggests that OSA is highly underdiagnosed and that it is an evolving process with gender specific progression. Females with OSA show significantly lower ventricular volumes while males with snoring show increased ejection fractions which may be an early sign of hypertrophy. Separate prospective studies are needed to further explore the direction of causality.

Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes: Insights From the UK Biobank Population Imaging Study.

Background: Exposure to ambient air pollution is strongly associated with increased cardiovascular morbidity and mortality. Little is known about the influence of air pollutants on cardiac structure and function. We aim to investigate the relationship between chronic past exposure to traffic-related pollutants and the cardiac chamber volume, ejection fraction, and left ventricular remodeling patterns after accounting for potential confounders. Methods: Exposure to ambient air pollutants including particulate matter and nitrogen dioxide was estimated from the Land Use Regression models for the years between 2005 and 2010. Cardiac parameters were measured from cardiovascular magnetic resonance imaging studies of 3920 individuals free from pre-existing cardiovascular disease in the UK Biobank population study. The median (interquartile range) duration between the year of exposure estimate and the imaging visit was 5.2 (0.6) years. We fitted multivariable linear regression models to investigate the relationship between cardiac parameters and traffic-related pollutants after adjusting for various confounders. Results: The studied cohort was 62±7 years old, and 46% were men. In fully adjusted models, particulate matter with an aerodynamic diameter <2.5 µm concentration was significantly associated with larger left ventricular end-diastolic volume and end-systolic volume (effect size = 0.82%, 95% CI, 0.09-1.55%, P=0.027; and effect size = 1.28%, 95% CI, 0.15-2.43%, P=0.027, respectively, per interquartile range increment in particulate matter with an aerodynamic diameter <2.5 µm) and right ventricular end-diastolic volume (effect size = 0.85%, 95% CI, 0.12-1.58%, P=0.023, per interquartile range increment in particulate matter with an aerodynamic diameter <2.5 µm). Likewise, higher nitrogen dioxide concentration was associated with larger biventricular volume. Distance from the major roads was the only metric associated with lower left ventricular mass (effect size = -0.74%, 95% CI, -1.3% to -0.18%, P=0.01, per interquartile range increment). Neither left and right atrial phenotypes nor left ventricular geometric remodeling patterns were influenced by the ambient pollutants. Conclusions: In a large asymptomatic population with no prevalent cardiovascular disease, higher past exposure to particulate matter with an aerodynamic diameter <2.5 µm and nitrogen dioxide was associated with cardiac ventricular dilatation, a marker of adverse remodeling that often precedes heart failure development.

Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study.

BACKGROUND: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. METHODS: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. RESULTS: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores. CONCLUSIONS: We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.

BACKGROUND: Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. METHODS: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). RESULTS: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. CONCLUSIONS: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.

Effect of renal artery revascularization upon cardiac structure and function in atherosclerotic renal artery stenosis: cardiac magnetic resonance sub-study of the ASTRAL trial.

BACKGROUND: Cardiac abnormalities are frequent in patients with atherosclerotic renovascular disease (ARVD). The Angioplasty and Stenting for Renal Artery Lesions (ASTRAL) trial studied the effect of percutaneous renal revascularization combined with medical therapy compared with medical therapy alone in 806 patients with ARVD. METHODS: This was a pre-specified sub-study of ASTRAL (clinical trials registration, current controlled trials number: ISRCTN59586944), designed to consider the effect of percutaneous renal artery angioplasty and stenting on change in cardiac structure and function, measured using cardiac magnetic resonance (CMR) imaging. Fifty-one patients were recruited from six selected ASTRAL centres. Forty-four completed the study (medical therapy n = 21; revascularization n = 23). Full analysis of CMR was possible in 40 patients (18 medical therapy and 22 revascularization). CMR measurements of left and right ventricular end systolic (LV and RVESV) and diastolic volume (LV and RVEDV), ejection fraction (LVEF) and mass (LVM) were made shortly after recruitment and before revascularization in the interventional group, and again after 12 months. Reporting was performed by CMR analysts blinded to randomization arm. RESULTS: Groups were well matched for mean age (70 versus 72 years), blood pressure (148/71 versus 143/74 mmHg), degree of renal artery stenosis (75 versus 75%) and comorbid conditions. In both randomized groups, improvements in cardiac structural parameters were seen at 12 months, but there were no significant differences between treatment groups. Median left ventricular changes between baseline and 12 months (medical versus revascularization) were LVEDV -1.9 versus -5.8 mL, P = 0.4; LVESV -2.1 versus 0.3 mL, P = 0.7; LVM -5.4 versus -6.3 g, P = 0.8; and LVEF -1.5 versus -0.8%, P = 0.7. Multivariate regression also found that randomized treatment assignment was not associated with degree of change in any of the CMR measurements. CONCLUSIONS: In this sub-study of the ASTRAL trial, renal revascularization did not offer additional benefit to cardiac structure or function in unselected patients with ARVD.

Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is the gold standard method for the assessment of cardiac structure and function. Reference ranges permit differentiation between normal and pathological states. To date, this study is the largest to provide CMR specific reference ranges for left ventricular, right ventricular, left atrial and right atrial structure and function derived from truly healthy Caucasian adults aged 45-74. METHODS: Five thousand sixty-five UK Biobank participants underwent CMR using steady-state free precession imaging at 1.5 Tesla. Manual analysis was performed for all four cardiac chambers. Participants with non-Caucasian ethnicity, known cardiovascular disease and other conditions known to affect cardiac chamber size and function were excluded. Remaining participants formed the healthy reference cohort; reference ranges were calculated and were stratified by gender and age (45-54, 55-64, 65-74). RESULTS: After applying exclusion criteria, 804 (16.2%) participants were available for analysis. Left ventricular (LV) volumes were larger in males compared to females for absolute and indexed values. With advancing age, LV volumes were mostly smaller in both sexes. LV ejection fraction was significantly greater in females compared to males (mean ± standard deviation [SD] of 61 ± 5% vs 58 ± 5%) and remained static with age for both genders. In older age groups, LV mass was lower in men, but remained virtually unchanged in women. LV mass was significantly higher in males compared to females (mean ± SD of 53 ± 9 g/m2 vs 42 ± 7 g/m2). Right ventricular (RV) volumes were significantly larger in males compared to females for absolute and indexed values and were smaller with advancing age. RV ejection fraction was higher with increasing age in females only. Left atrial (LA) maximal volume and stroke volume were significantly larger in males compared to females for absolute values but not for indexed values. LA ejection fraction was similar for both sexes. Right atrial (RA) maximal volume was significantly larger in males for both absolute and indexed values, while RA ejection fraction was significantly higher in females. CONCLUSIONS: We describe age- and sex-specific reference ranges for the left ventricle, right ventricle and atria in the largest validated normal Caucasian population.

Left atrial function measured by cardiac magnetic resonance imaging in patients with heart failure: clinical associations and prognostic value.

BACKGROUND: Left atrial (LA) volume is an important marker of cardiac dysfunction and cardiovascular outcome in heart failure (HF), but LA function is rarely measured. METHODS: Left atrial emptying function (LAEF), its clinical associations and prognostic value was studied in outpatients referred with suspected HF who were in sinus rhythm and had cardiac magnetic resonance imaging (CMRI). Heart failure was defined as relevant symptoms and signs with either a left ventricular ejection fraction (LVEF) <50% or amino-terminal pro-B-type natriuretic peptide (NTproBNP) >400 pg/mL (or >125 pg/mL if taking loop diuretics). RESULTS: Of 982 patients, 664 fulfilled the HF criteria and were in sinus rhythm. The median (interquartile range, IQR) LAEF was 42 (31-51)% and 55 (48-61)% in patients with and without HF (P < 0.001). Patients with HF in the lowest quartile of LAEF (23%; IQR: 17-28%) had lower LV and right ventricular (RV) EF, and greater LV and RV mass and higher plasma NTproBNP than those in the highest quartile of LAEF (56%; IQR: 53-61%). Log[LAEF] and log[NTproBNP] were inversely correlated (r = -0.410, P < 0.001). During a median follow-up of 883 (IQR: 469-1626) days, 394 (59%) patients with HF died or were admitted with HF and 101 (15%) developed atrial fibrillation (AF). In a multivariable Cox model, increasing LAEF, but not LVEF, was independently associated with survival (HR for 10% change: 0.81 (95%CI: 0.73-0.90), P = <0.001). Increasing age and decreasing LAEF predicted incident AF. CONCLUSIONS: In patients with HF, LAEF predicts adverse outcome independently of other measures of cardiac dysfunction.

Misclassification of females and males in cardiovascular magnetic resonance parametric mapping: the importance of sex-specific normal ranges for diagnosis of health vs. disease.

AIMS: Cardiovascular magnetic resonance parametric mapping enables non-invasive quantitative myocardial tissue characterization. Human myocardium has normal ranges of T1 and T2 values, deviation from which may indicate disease or change in physiology. Normal myocardial T1 and T2 values are affected by biological sex. Consequently, normal ranges created with insufficient numbers of each sex may result in sampling biases, misclassification of healthy values vs. disease, and even misdiagnoses. In this study, we investigated the impact of using male normal ranges for classifying female cases as normal or abnormal (and vice versa). METHODS AND RESULTS: One hundred and forty-two healthy volunteers (male and female) were scanned on two Siemens 3T MR systems, providing averaged global myocardial T1 and T2 values on a per-subject basis. The Monte Carlo method was used to generate simulated normal ranges from these values to estimate the statistical accuracy of classifying healthy female or male cases correctly as 'normal' when using sex-specific vs. mixed-sex normal ranges. The normal male and female T1- and T2-mapping values were significantly different by sex, after adjusting for age and heart rate. CONCLUSION: Using 15 healthy volunteers who are not sex specific to establish a normal range resulted in a typical misclassification of up to 36% of healthy females and 37% of healthy males as having abnormal T1 values and up to 16% of healthy females and 12% of healthy males as having abnormal T2 values. This paper highlights the potential adverse impact on diagnostic accuracy that can occur when local normal ranges contain insufficient numbers of both sexes. Sex-specific reference ranges should thus be routinely adopted in clinical practice.

Left ventricular anatomy in obstructive hypertrophic cardiomyopathy: beyond basal septal hypertrophy.

AIMS: Obstructive hypertrophic cardiomyopathy (oHCM) is characterized by dynamic obstruction of the left ventricular (LV) outflow tract (LVOT). Although this may be mediated by interplay between the hypertrophied septal wall, systolic anterior motion of the mitral valve, and papillary muscle abnormalities, the mechanistic role of LV shape is still not fully understood. This study sought to identify the LV end-diastolic morphology underpinning oHCM. METHODS AND RESULTS: Cardiovascular magnetic resonance images from 2398 HCM individuals were obtained as part of the NHLBI HCM Registry. Three-dimensional LV models were constructed and used, together with a principal component analysis, to build a statistical shape model capturing shape variations. A set of linear discriminant axes were built to define and quantify (Z-scores) the characteristic LV morphology associated with LVOT obstruction (LVOTO) under different physiological conditions and the relationship between LV phenotype and genotype. The LV remodelling pattern in oHCM consisted not only of basal septal hypertrophy but a combination with LV lengthening, apical dilatation, and LVOT inward remodelling. Salient differences were observed between obstructive cases at rest and stress. Genotype negative cases showed a tendency towards more obstructive phenotypes both at rest and stress. CONCLUSIONS: LV anatomy underpinning oHCM consists of basal septal hypertrophy, apical dilatation, LV lengthening, and LVOT inward remodelling. Differences between oHCM cases at rest and stress, as well as the relationship between LV phenotype and genotype, suggest different mechanisms for LVOTO. Proposed Z-scores render an opportunity of redefining management strategies based on the relationship between LV anatomy and LVOTO.

Atherosclerotic disease of the abdominal aorta and its branches: prognostic implications in patients with heart failure.

Aortic atherosclerosis reduces compliance in the systemic circulation and increases peripheral resistance, afterload and left ventricular wall stress. In patients with heart failure, these changes can impair left ventricular systolic function and energy efficiency, which could reduce exercise capacity. Though the interaction and the impact of aortic atherosclerosis on left ventricular function have been investigated, its prognostic implications in patients with heart failure are unclear. We used cardiac magnetic resonance imaging and gadolinium-enhanced abdominal aortography to investigate the prevalence and prognostic impact of atherosclerotic disease of the abdominal aorta and its side branches in 355 patients with heart failure. Sclerotic abdominal aortic disease was defined as a luminal narrowing >50% of the aorta and its side branches or the presence of abdominal aortic aneurysm. Patients with disease of the aorta and its branches were older (P < 0.0001), had overall longer stay in hospital (P = 0.006) and had more admissions (P = 0.001) and worse prognosis (hazard ratio: 1.97, 95% confidence interval: 1.29-3.00, P = 0.002) than those without. In a multivariable model, increasing age and pulse pressure, diabetes mellitus and increasing left ventricular end-diastolic volume were associated with a worse prognosis, but sclerotic abdominal aortic disease was not independently related to outcome (hazard ratio: 1.06; 95% confidence interval: 0.64-1.74; P = 0.823). These data demonstrate that atherosclerosis of the abdominal aorta and its side branches is common and associated with increased morbidity in patients with chronic heart failure. How such disease should be managed remains uncertain, but its recognition and characterisation are the first steps in finding out.

Prevalence of scarred and dysfunctional myocardium in patients with heart failure of ischaemic origin: a cardiovascular magnetic resonance study.

BACKGROUND: Cardiovascular magnetic resonance (CMR) with late gadolinium enhancement (LGE) can provide unique data on the transmural extent of scar/viability. We assessed the prevalence of dysfunctional myocardium, including partial thickness scar, which could contribute to left ventricular contractile dysfunction in patients with heart failure and ischaemic heart disease who denied angina symptoms. METHODS: We invited patients with ischaemic heart disease and a left ventricular ejection fraction < 50% by echocardiography to have LGE CMR. Myocardial contractility and transmural extent of scar were assessed using a 17-segment model. RESULTS: The median age of the 193 patients enrolled was 70 (interquartile range: 63-76) years and 167 (87%) were men. Of 3281 myocardial segments assessed, 1759 (54%) were dysfunctional, of which 581 (33%) showed no scar, 623 (35%) had scar affecting ≤50% of wall thickness and 555 (32%) had scar affecting > 50% of wall thickness. Of 1522 segments with normal contractile function, only 98 (6%) had evidence of scar on CMR. Overall, 182 (94%) patients had ≥1 and 107 (55%) patients had ≥5 segments with contractile dysfunction that had no scar or ≤50% transmural scar suggesting viability. CONCLUSIONS: In this cohort of patients with left ventricular systolic dysfunction and ischaemic heart disease, about half of all segments had contractile dysfunction but only one third of these had > 50% of the wall thickness affected by scar, suggesting that most dysfunctional segments could improve in response to an appropriate intervention.

Standardization of T1-mapping in cardiovascular magnetic resonance using clustered structuring for benchmarking normal ranges.

BACKGROUND: Cardiovascular magnetic resonance T1-mapping is increasingly used for tissue characterization, commonly based on Modified Look-Locker Inversion recovery (MOLLI). However, there are numerous MOLLI variants with differing normal ranges. This lack of standardization presents confusion and difficulty in inter-center comparisons, hindering widespread adoption of T1-mapping. METHODS: To address this, we performed a structured literature search for native left ventricular myocardial T1-mapping in healthy humans measured using MOLLI variants at 1.5 and 3 Tesla, across scanner vendors. We then used k-means clustering to structure normal MOLLI-T1 values according to magnetic field strength, and investigated correlations between common imaging parameters: repetition time (TR), echo time (TE), flip angle (FA). RESULTS: We analyzed data from 2207 healthy controls in 76 independent reports. Normal MOLLI-T1 standard deviations varied by 11-fold, and dependencies on TE, TR, and FA differed between 1.5 T and 3 T, thwarting meaningful T1 standardization even within a single field strength, including the use of Z-score. However, divergent MOLLI-T1 norms may be structured using data clustering. For 1.5 T, two clusters emerged: Cluster11.5T: T1 = 958 ± 16 ms (n = 1280); Cluster21.5T: T1 = 1027 ± 19 ms (n = 386). For 3 T, three clusters emerged: Cluster13T: T1 = 1160 ± 21 ms (n = 330); Cluster23T: T1 = 1067 ± 18 ms (n = 178); Cluster33T: T1 = 1227 ± 19 ms (n = 41). We then propose the concept of an online calculator for assigning local norms to a known MOLLI-T1 cluster, allowing benchmarking against published norms. CONCLUSION: Clustered structuring allows T1 standardization of widely-divergent MOLLI variants, benchmarking local norms (usually based on smaller samples) against published norms (larger samples). This may increase confidence and quality control in method implementation, facilitating wider clinical adoption of T1-mapping.

MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps. Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 samples with a diverse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion). Results: MOCOnet achieved fast image registration (<1 second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 (p < 0.001), whereas the baseline method reduced it to 15.8±15.6 (p < 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently (p = 0.007). Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.

Sex-specific associations between alcohol consumption, cardiac morphology, and function as assessed by magnetic resonance imaging: insights form the UK Biobank Population Study.

AIMS: Data regarding the effects of regular alcohol consumption on cardiac anatomy and function are scarce. Therefore, we sought to determine the relationship between regular alcohol intake and cardiac structure and function as evaluated with cardiac magnetic resonance imaging. METHODS AND RESULTS: Participants of the UK Biobank who underwent cardiac magnetic resonance were enrolled in our analysis. Data regarding regular alcohol consumption were obtained from questionnaires filled in by the study participants. Exclusion criteria were poor image quality, missing, or incongruent data regarding alcohol drinking habits, prior drinking, presence of heart failure or angina, and prior myocardial infarction or stroke. Overall, 4335 participants (61.5 ± 7.5 years, 47.6% male) were analysed. We used multivariate linear regression models adjusted for age, ethnicity, body mass index, smoking, hypertension, diabetes mellitus, physical activity, cholesterol level, and Townsend deprivation index to examine the relationship between regular alcohol intake and cardiac structure and function. In men, alcohol intake was independently associated with marginally increased left ventricular end-diastolic volume [ß = 0.14; 95% confidence interval (CI) = 0.05-0.24; P = 0.004], left ventricular stroke volume (ß = 0.08; 95% CI = 0.03-0.14; P = 0.005), and right ventricular stroke volume (ß = 0.08; 95% CI = 0.02-0.13; P = 0.006). In women, alcohol consumption was associated with increased left atrium volume (ß = 0.14; 95% CI = 0.04-0.23; P = 0.006). CONCLUSION: Alcohol consumption is independently associated with a marginal increase in left and right ventricular volumes in men, but not in women, whereas alcohol intake showed an association with increased left atrium volume in women. Our results suggest that there is only minimal relationship between regular alcohol consumption and cardiac morphology and function in an asymptomatic middle-aged population.

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious and prone to error. We present a deep learning approach with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping. Firstly, we customised a multi-stream Convolutional Neural Network (CNN) image classifier to streamline the process of automatic motion artefact detection. Secondly, we imposed attention supervision to guide the CNN to focus on targeted myocardial segments. Thirdly, when there was disagreement between the human operator and machine, a second human validator reviewed and rescored the cases for adjudication and to identify the source of disagreement. The multi-stream neural networks demonstrated 89.8% agreement, 87.4% ROC-AUC on motion artefact detection with the human operator in the 2568 T1 maps. Trained with additional supervision on attention, agreements and AUC significantly improved to 91.5% and 89.1%, respectively (p < 0.001). Rescoring of disagreed cases by the second human validator revealed that human operator error was the primary cause of disagreement. Deep learning with attention supervision provides a quick and high-quality assurance of clinical images, and outperforms human operators.

Standardized image post-processing of cardiovascular magnetic resonance T1-mapping reduces variability and improves accuracy and consistency in myocardial tissue characterization.

BACKGROUND: Myocardial T1-mapping is increasingly used in multicentre studies and trials. Inconsistent image analysis introduces variability, hinders differentiation of diseases, and results in larger sample sizes. We present a systematic approach to standardize T1-map analysis by human operators to improve accuracy and consistency. METHODS: We developed a multi-step training program for T1-map post-processing. The training dataset contained 42 left ventricular (LV) short-axis T1-maps (normal and diseases; 1.5 and 3 Tesla). Contours drawn by two experienced human operators served as reference for myocardial T1 and wall thickness (WT). Trainees (n = 26) underwent training and were evaluated by: (a) qualitative review of contours; (b) quantitative comparison with reference T1 and WT. RESULTS: The mean absolute difference between reference operators was 8.4 ±â€¯6.3 ms (T1) and 1.2 ±â€¯0.7 pixels (WT). Trainees' mean discrepancy from reference in T1 improved significantly post-training (from 8.1 ±â€¯2.4 to 6.7 ±â€¯1.4 ms; p < 0.001), with a 43% reduction in standard deviation (SD) (p = 0.035). WT also improved significantly post-training (from 0.9 ±â€¯0.4 to 0.7 ±â€¯0.2 pixels, p = 0.036), with 47% reduction in SD (p = 0.04). These experimentally-derived thresholds served to guide the training process: T1 (±8 ms) and WT (±1 pixel) from reference. CONCLUSION: A standardized approach to CMR T1-map image post-processing leads to significant improvements in the accuracy and consistency of LV myocardial T1 values and wall thickness. Improving consistency between operators can translate into 33-72% reduction in clinical trial sample-sizes. This work may: (a) serve as a basis for re-certification for core-lab operators; (b) translate to sample-size reductions for clinical studies; (c) produce better-quality training datasets for machine learning.