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.

Acute vasodilator response testing in the adult Fontan circulation using non-invasive 4D Flow MRI: a proof-of-principle study.

BACKGROUND: Pulmonary vasodilator therapy in Fontan patients can improve exercise tolerance. We aimed to assess the potential for non-invasive testing of acute vasodilator response using four-dimensional (D) flow MRI during oxygen inhalation. MATERIALS AND METHODS: Six patients with well-functioning Fontan circulations were prospectively recruited and underwent cardiac MRI. Ventricular anatomical imaging and 4D Flow MRI were acquired at baseline and during inhalation of oxygen. Data were compared with six age-matched healthy volunteers with 4D Flow MRI scans acquired at baseline. RESULTS: All six patients tolerated the MRI scan well. The dominant ventricle had a left ventricular morphology in all cases. On 4D Flow MRI assessment, two patients (Patients 2 and 6) showed improved cardiac filling with improved preload during oxygen administration, increased mitral inflow, increased maximum E-wave kinetic energy, and decreased systolic peak kinetic energy. Patient 1 showed improved preload only. Patient 5 showed no change, and patient 3 had equivocal results. Patient 4, however, showed a decrease in preload and cardiac filling/function with oxygen. DISCUSSION: Using oxygen as a pulmonary vasodilator to assess increased pulmonary venous return as a marker for positive acute vasodilator response would provide pre-treatment assessment in a more physiological state - the awake patient. This proof-of-concept study showed that it is well tolerated and has shown changes in some stable patients with a Fontan circulation.

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.

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.

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.

Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development.

BACKGROUND: The genetic basis of left ventricular (LV) image-derived phenotypes, which play a vital role in the diagnosis, management, and risk stratification of cardiovascular diseases, is unclear at present. METHODS: The LV parameters were measured from the cardiovascular magnetic resonance studies of the UK Biobank. Genotyping was done using Affymetrix arrays, augmented by imputation. We performed genome-wide association studies of 6 LV traits-LV end-diastolic volume, LV end-systolic volume, LV stroke volume, LV ejection fraction, LV mass, and LV mass to end-diastolic volume ratio. The replication analysis was performed in the MESA study (Multi-Ethnic Study of Atherosclerosis). We identified the candidate genes at genome-wide significant loci based on the evidence from extensive bioinformatic analyses. Polygenic risk scores were constructed from the summary statistics of LV genome-wide association studies to predict the heart failure events. RESULTS: The study comprised 16 923 European UK Biobank participants (mean age 62.5 years; 45.8% men) without prevalent myocardial infarction or heart failure. We discovered 14 genome-wide significant loci (3 loci each for LV end-diastolic volume, LV end-systolic volume, and LV mass to end-diastolic volume ratio; 4 loci for LV ejection fraction, and 1 locus for LV mass) at a stringent P<1×10-8. Three loci were replicated at Bonferroni significance and 7 loci at nominal significance (P<0.05 with concordant direction of effect) in the MESA study (n=4383). Follow-up bioinformatic analyses identified 28 candidate genes that were enriched in the cardiac developmental pathways and regulation of the LV contractile mechanism. Eight genes (TTN, BAG3, GRK5, HSPB7, MTSS1, ALPK3, NMB, and MMP11) supported by at least 2 independent lines of in silico evidence were implicated in the cardiac morphogenesis and heart failure development. The polygenic risk scores of LV phenotypes were predictive of heart failure in a holdout UK Biobank sample of 3106 cases and 224 134 controls (odds ratio 1.41, 95% CI 1.26 - 1.58, for the top quintile versus the bottom quintile of the LV end-systolic volume risk score). CONCLUSIONS: We report 14 genetic loci and indicate several candidate genes that not only enhance our understanding of the genetic architecture of prognostically important LV phenotypes but also shed light on potential novel therapeutic targets for LV remodeling.

Cardiovascular magnetic resonance reference values of mitral and tricuspid annular dimensions: the UK Biobank cohort.

BACKGROUND: Mitral valve (MV) and tricuspid valve (TV) apparatus geometry are essential to define mechanisms and etiologies of regurgitation and to inform surgical or transcatheter interventions. Given the increasing use of cardiovascular magnetic resonance (CMR) for the evaluation of valvular heart disease, we aimed to establish CMR-derived age- and sex-specific reference values for mitral annular (MA) and tricuspid annular (TA) dimensions and tethering indices derived from truly healthy Caucasian adults. METHODS: 5065 consecutive UK Biobank participants underwent CMR using cine balanced steady-state free precession imaging at 1.5 T. Participants with non-Caucasian ethnicity, prevalent cardiovascular disease and other conditions known to affect cardiac chamber size and function were excluded. Absolute and indexed reference ranges for MA and TA diameters and tethering indices were stratified by gender and age (45-54, 55-64, 65-74 years). RESULTS: Overall, 721 (14.2%) truly healthy participants aged 45-74 years (54% women) formed the reference cohort. Absolute MA and TA diameters, MV tenting length and MV tenting area, were significantly larger in men. Mean ± standard deviation (SD) end-diastolic and end-systolic MA diameters in the 3-chamber view (anteroposterior diameter) were 2.9 ± 0.4 cm (1.5 ± 0.2 cm/m2) and 3.3 ± 0.4 cm (1.7 ± 0.2 cm/m2) in men, and 2.6 ± 0.4 cm (1.6 ± 0.2 cm/m2) and 3.0 ± 0.4 cm (1.8 ± 0.2 cm/m2) in women, respectively. Mean ± SD end-diastolic and end-systolic TA diameters in the 4-chamber view were 3.2 ± 0.5 cm (1.6 ± 0.3 cm/m2) and 3.2 ± 0.5 cm (1.7 ± 0.3 cm/m2) in men, and 2.9 ± 0.4 cm (1.7 ± 0.2 cm/m2) and 2.8 ± 0.4 cm (1.7 ± 0.3 cm/m2) in women, respectively. With advancing age, end-diastolic TA diameter became larger and posterior MV leaflet angle smaller in both sexes. Reproducibility of measurements was good to excellent with an inter-rater intraclass correlation coefficient (ICC) between 0.92 and 0.98 and an intra-rater ICC between 0.90 and 0.97. CONCLUSIONS: We described age- and sex-specific reference ranges of MA and TA dimensions and tethering indices in the largest validated healthy Caucasian population. Reference ranges presented in this study may help to improve the distinction between normal and pathological states, prompting the identification of subjects that may benefit from advanced cardiac imaging for annular sizing and planning of valvular interventions.

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.

Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank.

BACKGROUND: The associations between cardiovascular disease (CVD) risk factors and the biventricular geometry of the right ventricle (RV) and left ventricle (LV) have been difficult to assess, due to subtle and complex shape changes. We sought to quantify reference RV morphology as well as biventricular variations associated with common cardiovascular risk factors. METHODS: A biventricular shape atlas was automatically constructed using contours and landmarks from 4329 UK Biobank cardiovascular magnetic resonance (CMR) studies. A subdivision surface geometric mesh was customized to the contours using a diffeomorphic registration algorithm, with automatic correction of slice shifts due to differences in breath-hold position. A reference sub-cohort was identified consisting of 630 participants with no CVD risk factors. Morphometric scores were computed using linear regression to quantify shape variations associated with four risk factors (high cholesterol, high blood pressure, obesity and smoking) and three disease factors (diabetes, previous myocardial infarction and angina). RESULTS: The atlas construction led to an accurate representation of 3D shapes at end-diastole and end-systole, with acceptable fitting errors between surfaces and contours (average error less than 1.5 mm). Atlas shape features had stronger associations than traditional mass and volume measures for all factors (p < 0.005 for each). High blood pressure was associated with outward displacement of the LV free walls, but inward displacement of the RV free wall and thickening of the septum. Smoking was associated with a rounder RV with inward displacement of the RV free wall and increased relative wall thickness. CONCLUSION: Morphometric relationships between biventricular shape and cardiovascular risk factors in a large cohort show complex interactions between RV and LV morphology. These can be quantified by z-scores, which can be used to study the morphological correlates of disease.

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.

State-of-the-art review: stress T1 mapping-technical considerations, pitfalls and emerging clinical applications.

In vivo mapping of the myocardial T1 relaxation time has recently attained wide clinical validation of its potential utility. In this review, we address the basic principles of the T1 mapping techniques, with particular attention to the emerging application of vasodilatory stress agents to interrogate the myocardial microvascular compartment, and differences between commonly used T1 mapping methods when applied in clinical practice.

Measurement of myocardial native T1 in cardiovascular diseases and norm in 1291 subjects.

BACKGROUND: Native T1-mapping provides quantitative myocardial tissue characterization for cardiovascular diseases (CVD), without the need for gadolinium. However, its translation into clinical practice is hindered by differences between techniques and the lack of established reference values. We provide typical myocardial T1-ranges for 18 commonly encountered CVDs using a single T1-mapping technique - Shortened Look-Locker Inversion Recovery (ShMOLLI), also used in the large UK Biobank and Hypertrophic Cardiomyopathy Registry study. METHODS: We analyzed 1291 subjects who underwent CMR (1.5-Tesla, MAGNETOM-Avanto, Siemens Healthcare, Erlangen, Germany) between 2009 and 2016, who had a single CVD diagnosis, with mid-ventricular T1-map assessment. A region of interest (ROI) was placed on native T1-maps in the "most-affected myocardium", characterized by the presence of late gadolinium enhancement (LGE), or regional wall motion abnormalities (RWMA) on cines. Another ROI was placed in the "reference myocardium" as far as possible from LGE/RWMA, and in the septum if no focal abnormality was present. To further define normality, we included native T1 of healthy subjects from an existing dataset after sub-endocardial pixel-erosions. RESULTS: Native T1 of patients with normal CMR (938 ± 21 ms) was similar compared to healthy subjects (941 ± 23 ms). Across all patient groups (57 ± 19 yrs., 65% males), focally affected myocardium had significantly different T1 value compared to reference myocardium (all p < 0.001). In the affected myocardium, cardiac amyloidosis (1119 ± 61 ms) had the highest native T1 compared to normal and all other CVDs, while iron-overload (795 ± 58 ms) and Anderson-Fabry disease (863 ± 23 ms) had the lowest native reference T1 (all p < 0.001). Future studies designed to detect the large T1 differences between affected and reference myocardium are estimated to require small sample-sizes (n < 50). However, studies designed to detect the small T1 differences between reference myocardium in CVDs and healthy controls can require several thousand of subjects. CONCLUSIONS: We provide typical T1-ranges for common clinical cardiac conditions in the largest cohort to-date, using ShMOLLI T1-mapping at 1.5 T. Sample-size calculations from this study may be useful for the design of future studies and trials that use T1-mapping as an endpoint.

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.

Adenosine stress CMR T1-mapping detects early microvascular dysfunction in patients with type 2 diabetes mellitus without obstructive coronary artery disease.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with coronary microvascular dysfunction in the absence of obstructive coronary artery disease (CAD). Cardiovascular magnetic resonance (CMR) T1-mapping at rest and during adenosine stress can assess coronary vascular reactivity. We hypothesised that the non-contrast T1 response to vasodilator stress will be altered in patients with T2DM without CAD compared to controls due to coronary microvascular dysfunction. METHODS: Thirty-one patients with T2DM and sixteen matched healthy controls underwent CMR (3 T) for cine, rest and adenosine stress non-contrast T1-mapping (ShMOLLI), first-pass perfusion and late gadolinium enhancement (LGE) imaging. Significant CAD (>50% coronary luminal stenosis) was excluded in all patients by coronary computed tomographic angiography. RESULTS: All subjects had normal left ventricular (LV) ejection and LV mass index, with no LGE. Myocardial perfusion reserve index (MPRI) was lower in T2DM than in controls (1.60 ± 0.44 vs 2.01 ± 0.42; p = 0.008). There was no difference in rest native T1 values (p = 0.59). During adenosine stress, T1 values increased significantly in both T2DM patients (from 1196 ± 32 ms to 1244 ± 44 ms, p < 0.001) and controls (from 1194 ± 26 ms to 1273 ± 44 ms, p < 0.001). T2DM patients showed blunted relative stress non-contrast T1 response (T2DM: ΔT1 = 4.1 ± 2.9% vs. CONTROLS: ΔT1 = 6.6 ± 2.6%, p = 0.007) due to a blunted maximal T1 during adenosine stress (T2DM 1244 ± 44 ms vs. controls 1273 ± 44 ms, p = 0.045). CONCLUSIONS: Patients with well controlled T2DM, even in the absence of arterial hypertension and significant CAD, exhibit blunted maximal non-contrast T1 response during adenosine vasodilatory stress, likely reflecting coronary microvascular dysfunction. Adenosine stress and rest T1 mapping can detect subclinical abnormalities of the coronary microvasculature, without the need for gadolinium contrast agents. CMR may identify early features of the diabetic heart phenotype and subclinical cardiac risk markers in patients with T2DM, providing an opportunity for early therapeutic intervention.

Correction to: Adenosine stress CMR T1-mapping detects early microvascular dysfunction in patients with type 2 diabetes mellitus without obstructive coronary artery disease.

In the original publication of this article [1] Fig. 1 was incorrect due to the use of a colour bar with wrong range in error.

Splenic T1-mapping: a novel quantitative method for assessing adenosine stress adequacy for cardiovascular magnetic resonance.

BACKGROUND: Perfusion cardiovascular magnetic resonance (CMR) performed with inadequate adenosine stress leads to false-negative results and suboptimal clinical management. The recently proposed marker of adequate stress, the "splenic switch-off" sign, detects splenic blood flow attenuation during stress perfusion (spleen appears dark), but can only be assessed after gadolinium first-pass, when it is too late to optimize the stress response. Reduction in splenic blood volume during adenosine stress is expected to shorten native splenic T1, which may predict splenic switch-off without the need for gadolinium. METHODS: Two-hundred and twelve subjects underwent adenosine stress CMR: 1.5 T (n = 104; 75 patients, 29 healthy controls); 3 T (n = 108; 86 patients, 22 healthy controls). Native T1spleen was assessed using heart-rate-independent ShMOLLI prototype sequence at rest and during adenosine stress (140 µg/kg/min, 4 min, IV) in 3 short-axis slices (basal, mid-ventricular, apical). This was compared with changes in peak splenic perfusion signal intensity (ΔSIspleen) and the "splenic switch-off" sign on conventional stress/rest gadolinium perfusion imaging. T1spleen values were obtained blinded to perfusion ΔSIspleen, both were derived using regions of interest carefully placed to avoid artefacts and partial-volume effects. RESULTS: Normal resting splenic T1 values were 1102 ± 66 ms (1.5 T) and 1352 ± 114 ms (3 T), slightly higher than in patients (1083 ± 59 ms, p = 0.04; 1295 ± 105 ms, p = 0.01, respectively). T1spleen decreased significantly during adenosine stress (mean ΔT1spleen ~ -40 ms), independent of field strength, age, gender, and cardiovascular diseases. While ΔT1spleen correlated strongly with ΔSIspleen (rho = 0.70, p < 0.0001); neither indices showed significant correlations with conventional hemodynamic markers (rate pressure product) during stress. By ROC analysis, a ΔT1spleen threshold of ≥ -30 ms during stress predicted the "splenic switch-off" sign (AUC 0.90, p < 0.0001) with sensitivity (90%), specificity (88%), accuracy (90%), PPV (98%), NPV (42%). CONCLUSIONS: Adenosine stress and rest splenic T1-mapping is a novel method for assessing stress responses, independent of conventional hemodynamic parameters. It enables prediction of the visual "splenic switch-off" sign without the need for gadolinium, and correlates well to changes in splenic signal intensity during stress/rest perfusion imaging. ΔT1spleen holds promise to facilitate optimization of stress responses before gadolinium first-pass perfusion CMR.

Cine dyscontractility index: A novel marker of mechanical dyssynchrony that predicts response to cardiac resynchronization therapy.

PURPOSE: To investigate whether magnetic resonance imaging (MRI) cine-derived dyssynchrony indices provide additional information compared to conventional tagged MRI (tMRI) acquisitions in heart failure patients undergoing cardiac resynchronization therapy (CRT). MATERIALS AND METHODS: Patients scheduled for CRT (n = 52) underwent preprocedure MRI including cine and tMRI acquisitions. Segmental strain curves were calculated for both cine and tMRI to produce a range of standard indices for direct comparison between modalities. We also proposed and evaluated a novel index of "dyscontractility," which detects the presence of focal areas with paradoxically positive circumferential strain. RESULTS: Across conventional strain indices, there was only moderate-to-poor (R = 0.3-0.6) correlation between modalities; eight cine-derived indices showed statistically significant (P < 0.05) relations to CRT outcome compared to just two tMRI-based counterparts. The novel dyscontractility index calculated on basal slice cine images (cine dyscontractility index, "CDI") was the single best predictor of clinical response to CRT (area under the curve AUC = 0.81, P < 0.001). While poorly correlated to its tMRI counterpart (R = 0.33), CDI performed significantly better in predicting response to CRT (P < 0.005), and was also numerically better than all other tMRI indices (AUC 0.53-0.76, all P for AUC comparisons <0.17). CONCLUSION: Cine-derived strain indices offer potentially new information compared to tMRI. Specifically, the novel CDI is most strongly linked to response to cardiac resynchronization therapy in a contemporary patient cohort. It utilizes readily available MRI data, is relatively straightforward to process, and compares favorably with any conventional tagging index. J. Magn. Reson. Imaging 2016;44:1483-1492.