Measurement of 24-h continuous human CH4 release in a whole room indirect calorimeter

We describe the technology and validation of a new whole room indirect calorimeter (WRIC) methodology to quantify volume of methane (VCH4) released from the human body over 24 h concurrently with the assessment of energy expenditure and substrate utilization. The new system extends the assessment of energy metabolism by adding CH4, a downstream product of microbiome fermentation that could contribute to energy balance. Our new system consists of an established WRIC combined with the addition of off-axis integrated-cavity output spectroscopy (OA-ICOS) to measure CH4 concentration ([CH4]). Development, validation, and reliability of the system included environmental experiments to measure the stability of the atmospheric [CH4], infusing CH4 into the WRIC and human cross-validation studies comparing [CH4] quantified by OA-ICOS and mid-infrared dual-comb spectroscopy (MIR DCS).Our infusion data indicated that the system measured 24-h [CH4] and VCH4 with high sensitivity, reliability, and validity. Cross-validation studies showed good agreement between OA-ICOS and MIR DCS technologies (r = 0.979, P < 0.0001). Human data revealed 24-h VCH4 was highly variable between subjects and within/between days. Finally, our method to quantify VCH4 released by breath or colon suggested that over 50% of the CH4 was eliminated through the breath. The method allows, for the first time, measurement of 24-h VCH4 (in kcal) and therefore the measurement of the proportion of human energy intake fermented to CH4 by the gut microbiome and released via breath or from the intestine; also, it allows us to track the effects of dietary, probiotic, bacterial, and fecal microbiota transplantation on VCH4.NEW & NOTEWORTHY This is the first time that continuous assessment of CH4 is reported in parallel with measurements of O2 consumption and CO2 production inside a whole room indirect calorimeter in humans and over 24 h. We provide a detailed description of the whole system and its parts. We carried out studies of reliability and validity of the whole system and its parts. CH4 is released in humans during daily activities.

Evaluation of interventions for essential newborn care practices at home-based delivery in India

 

India has the largest share of neonatal mortality, accounting for 21.7 per 1,000 in 2019, while the global goal is at least 12 per 1,000 by 2030. More than 20% of deliveries still occur at home in India for various reasons. Several national interventions were designed to ensure essential practices even at home by engaging skilled birth attendants (SBAs), antenatal care (ANC), and community health workers (CHWs). This study evaluates the effectiveness of these interventions on essential newborn care practices (EP); clean cord care, thermal care, and breastfeeding. Using data from the 2015-2016 India Demographic and Health Survey (n = 9,273), this study employs structural equation modeling to confirm the relationship between SBA, ANC, CHWs` counseling, and EP, including indirect effects of ANC and CHWs and moderating effects of women's empowerment. The results show that SBA and ANC have significant direct effects (standardised coefficient=0.105 and 0.056, respectively) on EP, and ANC and CHWs have significant indirect effects (0.015 and 0.004) in the well-fitted model (CFI=0.938, TLI=0.920, RMSEA (upper 90% CI)=0.028 (0.029), SRMR=0.044). The empowerment-related factors which had a significant positive moderating effect on the paths from SBA to EP and from ANC to EP are decision-making power (0.007, 0.003), allowed mobility (0.002, 0.001), and education (0.009, 0.004). More than 90% of EP variance is not associated with the factors in this model (standardised coefficient=0.958). SBA demonstrated the most considerable effectiveness for EP, while ANCs and CHW indirectly impacted EP. Improving women's empowerment can be an effective strategy to enhance EP. Previous literature said that the other factors explaining EP could be quality of care, other interventions for a safe birth, and cultural characteristics. Policymakers are recommended to consider comprehensive factors to address barriers to safe home birth and design CHW's intervention to persuade SBA.

Key messages

Evaluation of primary health care system in Yangon Region, Myanmar: a mixed-method approach

Background

Many low- and middle-income countries and international organizations have invested resources to strengthen primary health care services. Despite efforts from the Ministry of Health on primary health care, barriers to accessing health care services and health inequality in Myanmar still exist. This study aimed to identify the challenges and unmet needs in the current primary health care services by assessing the experiences and perceptions of healthcare workers and local leaders in three townships (Htantabin, Hmawbi, and Taikkyi) in Yangon, Myanmar.

Methods

The study was conducted among healthcare professionals and community leaders in three townships. By adopting a mixed-method approach, a cross-sectional health needs assessment survey was conducted for quantitative data (n = 66), and focus group discussions (15 group discussions) were conducted online for qualitative data.

Results

As a result of the survey regarding six domains; hygiene, primary medical care, maternal and child health, infectious diseases, non-communicable diseases, and leadership, enhancing the management and leadership capacity had the lowest average score on the current achievement (2.81 out of 5), while strengthening infectious disease control service and accessibility was perceived as the highest mean on the priority of intervention (4.28 out of 5) and the impact of the intervention (4.7). The focus group discussions revealed that while specific infrastructures and equipment necessary for the category were addressed, the need for financial support has been the recurrent theme throughout the discussions.

Conclusions

Utilizing the World Health Organization’s six-building block framework, our findings suggest that a long-term targeted financial investment in the primary health care system is critical in Myanmar by increasing health care expenditure per capita. At the same time, related barriers and facilitators should be considered to optimize the effectiveness of prioritized interventions.

Key messages

• Health care providers and local leaders perceived the management and leadership capacity as the lowest current achievement.

• A long-term targeted financial investment in the primary health care system is critical in Myanmar.

Exercise induced coronary inflammation in masters athletes

Background

Chronic endurance exercise has been linked to increased prevalence of coronary artery disease (CAD) in male master athletes. Data are limited regarding the presence of exercise-induced coronary inflammation and its association with atherosclerosis in master endurance athletes. Human coronary inflammation can be detected non-invasively by imaging pericoronary adipose tissue (PCAT). We tested the hypothesis that chronic endurance exercise leads to increased prevalence of atherosclerosis via coronary inflammation.

Methods

Computed tomography coronary angiogram CTCA scans from 2 cohorts of master athletes and age-matched controls, without known risk factors for CAD, were analysed post-hoc and the PCAT attenuation index was calculated around the proximal right coronary artery (FAIRCA). The athletes and the healthy controls also underwent an electrocardiogram, an echocardiogram, a cardiopulmonary exercise test (CPET), a 24-hour Holter tape and a Cardiac Magnetic Resonance (CMR) scan.

Results

Scans from 243 masters endurance athletes (62% females) and 58 age and Framingham CAD risk score matched healthy controls were analysed. FAIRCA was significantly higher (less negative) in male masters athletes vs female masters athletes [−61.3 Hounsfield Units (HU) vs −62.8 HU, p=0.01], in male athletes vs male controls (−61.3 HU vs −68.6 HU, p&lt;0.001) and in female athletes vs female controls (−62,8 HU vs −67.5 HU, p=0.005). In female masters athletes, peak oxygen consumption during CPET (peak VO2) statistically significantly predicted the FAIRCA, F(1,146) = 22.62, p&lt;0.0001. There was no correlation between the FAIRCA and presence of atherosclerosis in male masters athletes.

Conclusions

Masters athletes show increased markers of coronary inflammation. This effect appears to be greater in male masters athletes and is associated with a higher peak VO2 in female masters athletes. However, we did not identify a link between coronary inflammation and coronary atherosclerosis in this cohort.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Cardiac Risk in the Young, UK

Improved diagnostic accuracy for apical hypertrophic cardiomyopathy

Introduction

The diagnosis of apical hypertrophic cardiomyopathy (ApHCM) is contingent on demonstrating apical maximum wall thickness (MWT) of ≥15mm; the same threshold as other HCM subtypes. However, the myocardium naturally tapers towards the apex in healthy individuals, so ≥15mm MWT is proportionately higher in the apex than in naturally thicker basal segments. Using cardiac magnetic resonance (CMR), relative ApHCM has been described (typical ECG features, loss of apical tapering, cavity obliteration but hypertrophy &lt;15mm). Wall thickness measurement using machine learning now exceeds human performance.

Purpose

We aimed to redefine the optimal diagnostic threshold for ApHCM using segment-specific criteria based on a large cohort of healthy control subjects.

Methods

Segmental wall thickness was measured using healthy subjects from the UK Biobank using a clinically validated machine learning algorithm1,2. A normative reference range was established for all 16 segments, conditioned to body surface area (BSA), sex and age. Derived segment-specific wall thickness thresholds were used to define optimal disease thresholds for patients clinically managed with overt (MWT ≥15mm) and relative ApHCM (MWT &lt;15mm, but typical ECG and imaging findings).

Results

4118 UK biobank subjects were used to define normal segmental thicknesses and reference ranges. These were applied to ApHCM (73 overt, 31 relative). There were no apical wall thickness age related differences. The upper limit of the 95% confidence interval corresponded to a combined maximum apical MWT for both males and females of 10.4mm using non-indexed measurement, or 5.6mm/m2 when indexed to BSA. Non-indexed segmental threshold identified 100% of ApHCM patients (true positives), 81% (25 of 31) relative ApHCM and 3% (115 of 4118) of healthy UK biobank subjects (false positives). Indexed segmental thresholds improved the diagnostic potential in relative ApHCM without an increase in false positives (100% of ApHCM patients, 84% (26 of 31) of relative ApHCM patients, and 3% healthy UK biobank (127 of 4118).

Conclusion

We propose new diagnostic criteria for ApHCM using segmental indexed apical wall thickness of &gt;5.6 mm/m2 to better identify inappropriate apical hypertrophy in those whose wall thickness does not meet current criteria for diagnosis.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): British Heart Foundation

Cardiac magnetic resonance imaging predictors of ventricular arrhythmia in mid-cavity obstructive hypertrophic cardiomyopathy

Background/Introduction

Left ventricular (LV) mid-cavity obstruction (LVMCO) in hypertrophic cardiomyopathy (HCM) is an uncommon phenotypic feature predisposed to the formation of myocardial fibrosis and apical aneurysms (LVAA). These features may be independently proarrhythmic, and LVAA is considered a class 2a indication for implantable cardioverter defibrillator (ICD) in current US, but not European guidelines for the primary prevention of sudden cardiac death (SCD). Cardiac magnetic resonance (CMR) imaging is the preferred modality for detecting these and other phenotypic features critical to SCD risk assessment.

Purpose

To assess the ability of CMR imaging parameters to predict occurrence of non-sustained ventricular tachycardia (NSVT) in HCM patients with Doppler-derived evidence of LVMCO.

Methods

Multi-modality imaging records were retrospectively assessed to identify HCM patients with Doppler-LVMCO and CMR scans. CMR images were assessed by an investigator blinded to clinical status. Late gadolinium enhancement (LGE) was quantified using the full-width, half-maximum technique. CMR imaging parameters were assessed for predictive ability using Cox proportional hazards during univariate and multivariate analyses, accounting for time to event (NSVT or censorship of follow-up).

Results

The study cohort included 58 patients (57±11 years, 74% male) with a median follow-up of 6.2 (IQR 4.3) years. Mean mid-cavity gradient was 33±23 mmHg. NSVT was detected in 27/58 (47%) patients, was 4 beats or longer in 23/27 (85%) and was monomorphic in 21/27 (77%).

On univariate analysis, predictors of NSVT during follow-up include LV mass index (HR 1.02, 95% CI 1.00–1.04, p=0.03), LGE in grams (HR 1.04, 95% CI 1.01–1.06, p=0.005), and LVAA (HR 2.57, 95% CI 1.14–5.79, p=0.023). After multivariate adjustment (Table 2), none were significantly associated.

Conclusions

In LVMCO, magnitude of LV hypertrophy, extent of LGE and the presence of an apical aneurysm may not be independent predictors of ventricular arrhythmias. SCD algorithms based on qualitative assessments of these features may overestimate risk.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): National Institute of Health Research (NIHR)

Extracellular volume fraction by computed tomography predicts long-term prognosis among patients with cardiac amyloidosis

Objective

This study sought to investigate the association of extracellular volume fraction by computed tomography (ECVCT), myocardial remodeling and mortality in patients with systemic amyloidosis.

Background

Light chain (AL) and transthyretin (ATTR) amyloid fibrils are deposited in the extracellular space of the myocardium, resulting in heart failure and premature mortality. Extracellular expansion can be quantified by CT, offering a rapid and cost-effective alternative to cardiovascular magnetic resonance (CMR), especially among patients with cardiac devices or on renal dialysis.

Methods

Patients with confirmed systemic amyloidosis and varying degrees of cardiac involvement underwent ECG-gated cardiac CT. ECVCT was analysed in the inter-ventricular septum. All patients also underwent clinical assessment, ECG, echocardiography, serum amyloid protein component (SAP) and/or technetium-99m (99mTc) 3,3-diphosphono-1,2-propanodicarboxylic acid scintigraphy. ECVCT was compared across different extents of cardiac infiltration (ATTR Perugini Grade / AL Mayo Class) and evaluated for its association with myocardial remodelling and all-cause mortality.

Results

72 patients were studied (AL n=35, ATTR n=37; age 67 (59–76) years, 71% males). Mean septal ECVCT was 42.7±13.1% and 55.8±10.9% in AL and ATTR, respectively, and correlated with indexed left ventricular (LV) mass (r=0.426, p&lt;0.001), LV ejection fraction [LVEF, (r=0.460, p&lt;0.001)], NT-proBNP (r=0.563, p&lt;0.001) and hsTnT (r=0.546, p=0.02). ECVCT increased with cardiac amyloid involvement in both AL and ATTR (Figure 1). Over a mean follow-up of 5.3±2.4 years, 40 deaths occurred (AL 14 [35%]; ATTR 26 [65%]). ECVCT was independently associated with all-cause mortality in ATTR (not AL) after adjustment for age and IV septal wall thickness (HR: 1.046, 95% CI: 1.003–1.090, p=0.037).

Conclusion

Cardiac amyloid burden quantified by ECVCT is associated with adverse cardiac remodelling as well as all-cause mortality among ATTR amyloid patients. ECVCT may address the need for better identification and risk stratification of amyloid patients, using a widely-accessible imaging modality (Figure 2).

Funding Acknowledgement

Type of funding sources: None.

Advanced microstructural substrate detection in pre-hypertrophic HCM and its relationship to arrhythmogenesis; a hybrid CMR-ECG-Imaging study

Background

Hypertrophic cardiomyopathy is defined in three domains; clinically by unexplained hypertrophy, genetically by sarcomeric gene mutations and histologically by disarray, small vessel disease and fibrosis. Both ischaemia and myocyte disarray have been implicated in arrhythmogenesis and sudden cardiac death but whether disarray occurs before hypertrophy and its relationship to ischaemia is unknown.

Diffusion-tensor CMR, perfusion mapping & ECG Imaging (ECGI) can measure disarray, ischaemia and electrical aberrance respectively in vivo. We aimed to investigate these in genotype positive (G+) subjects without hypertrophy (LVH−) to identify further subclinical manifestations of gene expression and whether these relate to ventricular arrhythmia formation.

Methods

Diffusion-tensor CMR (3-Tesla) using a motion-compensated spin-echo sequence was acquired in 3 short-axis slices. Quantitative adenosine stress perfusion mapping was performed using standard clinical protocols. A novel ECGI vest, containing 256 unipolar electrodes acquired a 5-minute recording of body-surface potentials to quantify conduction and repolarisation dynamics intervals.

Results

ECGI/CMR was performed on 68 mutation carriers from 64 families and 24 age sex and ethnicity matched healthy controls. Of the mutation carriers, median age was 33 (24–41 years), 57% (39) were female, and 79% (54) were white. Mutations were 39 (57%) MYBPC3, 19 (28%) MYH7, 1 (1%) MYL2 and 9 (12%) were thin filament/non-sarcomeric mutations. There was no significant difference in ejection fraction or LV mass, however G+LVH− had a higher maximum wall thickness (9 (9–10) vs 8 (7–9) mm p=0.003).

Compared to healthy volunteers, G+LVH− individuals had more perfusion defects (18/64 (30%) vs 0, p=0.004), lower Fractional Anisotropy (FA) (suggestive of more disarray) (0.32±0.02 vs 0.34±0.02, p&lt;0.0001) and more prolonged Activation–Recovery Intervals (ARI, a surrogate for action potential duration (259±40 vs 240±31 ms, p=0.03).

In G+LVH−, patients with perfusion defects had more prolonged ARI (263 (248 vs 292) vs 246 (225–283) ms, p=0.03) and lower FA suggestive of more disarray (0.32±0.2 vs 0.31±0.1, p=0.04).

Conclusion

Ischaemia, myocyte disarray and electrical abnormalities occur even in the absence of hypertrophy in HCM. These abnormalities associate to form a complex a clinical phenotype.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): British Heart FoundationBarts Charity

Quantitative myocardial blood flow as a prognostic marker for cardiovascular outcomes in patients with Type 2 Diabetes Mellitus: a multicentre study

 

Introduction: Patients with Type 2 Diabetes Mellitus (T2DM) are at increased risk of cardiovascular disease, including epicardial coronary heart disease, silent myocardial infarction (MI), and coronary microvascular dysfunction (CMD) [1]. All of these can be assessed and quantified using cardiac magnetic resonance (CMR), including most recently quantitative myocardial blood flow (MBF). We aimed to determine the prognostic relevance of MBF in patients with T2DM and test the hypothesis that impaired stress MBF and myocardial perfusion reserve (MPR) have independent prognostic value over standard clinical and imaging parameters.

Methods

A 4-centre study of patients with T2DM who underwent quantitative perfusion assessment using CMR. Diagnosis of T2DM was based on Hba1c &gt;48mmol/l or a known diagnosis of T2DM. Image analysis was performed automatically using an artificial intelligence approach deriving global MBF and MPR [2]. Cox proportional hazard models adjusting for comorbidities and CMR parameters sought associations between stress MBF and MPR with death and major adverse cardiovascular events (MACE), including MI, non-fatal stroke, heart failure hospitalisation and death.

Results

A total of 630 patients with T2DM were included with a median follow-up of 722 days (interquartile range 493) days. There were 27 (4.3%) deaths and 76 MACE events in 62 (12.1%) patients. Patient data was represented into groups depending on threshold stress MBF values of 1.94ml/g/min and MPR thresholds of 1.96 using validated data from invasive coronary physiology [3]. Patient demographics and CMR data are seen in table 1. Kaplan-Meier curves are seen in figure 1. Stress MBF was associated with mortality and MACE after adjusting for age, LV ejection fraction and HbA1c. The stress MBF adjusted hazard ratios for all cause death and death and MACE were 0.35 (95% CI, 0.13–0.95, P=0.04) and 0.54 (95% CI, 0.30–0.96, P=0.04), respectively. MPR was not significantly associated with death and MACE after adjusting for age, LV ejection fraction and HbA1c; hazard ratio for all cause death and death and MACE was 0.83 (95% CI, 0.41–1.69, P=0.60) and 0.81 (95% CI, 0.53–1.23, p=0.32) respectively.

Conclusion

In patients with T2DM, reduced stress MBF measured automatically inline using artificial intelligence quantification of cardiovascular magnetic resonance perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcome.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): 1. REC ID 14/EE/0007 (Barts Heart Centre funding). 2. For PREDICT (Leicester data) ethical approval was provided by the UK Health Research Authority Research Ethics Committee (reference 17/WM/0192). 3. MATCH Study, Leeds - British Heart Foundation - 17/YH/0300. 4. LEAN-DM, Leeds - British Heart Foundation - 18/YH/01685. CEED, Leeds: British Heart Foundation - REC reference - 18/YH/0190

Heterozygous APOE ε4 carriage associates with improved myocardial efficiency in older age

Background

Carriage of the ancestral APOE ε4 allele confers a risk of developing Alzheimer's and coronary artery disease, but its persistence in human populations also suggests some potential survival advantages. To date it remains unclear whether APOE ε4 carriage independently associates with a better or worse long-term cardiac phenotype.

Purpose

Using data from the 1946 National Survey of Health and Development (NSHD) birth cohort, we investigated whether APOE ε4 carriage associates with adverse or beneficial left ventricular (LV) size and function parameters by echocardiography in older age.

Methods

Based on the presence or absence of APOE ε4, genotypes were divided into: non-APOE ε4 (ε2ε2, ε2ε3, ε3ε3), heterozygous-APOE ε4 (ε2ε4 and ε3ε4) and homozygous-APOE ε4 (ε4ε4). Echocardiographic data at 60–64 years included: left ventricular ejection fraction (LV EF), E/e', systolic and diastolic LV posterior wall and interventricular septal thickness (LVPWTs/d, IVSs/d), and body-surface area indexed LV mass (LVmassi) and myocardial contraction fraction (MCFi). Generalized linear models explored associations between APOE ε4 genotypes as exposures and echocardiographic biomarkers as outcomes. As a combination of gene variants, APOE ε genotype is expected to be an instrumental variable and therefore unconfounded. Thus, Model 1 was unadjusted. To obtain more precise regression estimates, Model 2 was adjusted for factors associated with the outcome, namely sex and socio-economic position (SEP). To explore the mechanistic pathway downstream of APOE ε genotype but upstream of the echocardiographic outcomes, subsequent models were adjusted for mediators as follows: Model 3 for body mass index, Model 4 for the presence of cardiovascular disease (CVD), Model 5 for diabetes, Model 6 for high cholesterol and Model 7 for hypertension.

Results

1464 participants were included. Compared to non-APOE ε4 and homozygous groups, heterozygous-APOE ε4 individuals had similar cardiac phenotypes in terms of EF, E/e', LVPWTs/d, IVSs/d and LVmassi but had a 7% higher MCFi 95% confidence interval [CI]: 1%-13%, p=0.017) which persisted even after adjustment for sex and SEP (95% CI 1%-12%, p=0.026) that was attenuated to 6% after adjustment for CVD (95% CI 0–13% p=0.050) and hypertension (95% CI 1–13% p=0.022).

Conclusion

The heterozygous-APOE ε4 state associates with improved myocardial shortening in older age resulting in greater LV stroke volume generation per 1 mL of myocardium. As we found no association between APOE ε4 carriage and LVPWTs/d, IVSs/d or LVmassi, MCFi enhancement may be mediated by improved myocardial energetics and contractility, with calcium and androgens potentially implicated, rather than through pathological ventricular thickening. Although a dose relationship is normally expected with ε4 carriage, any benefit from increased energetics and contractility is likely to be counterbalances by the higher risk of CVD and cardiovascular risk factors.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Medical Research Council British Heart Foundation

Convolutional neural network transformer (CNNT) for free-breathing real-time cine imaging

Funding Acknowledgements

Type of funding sources: None.

Background

Real-time cine imaging does not require breath-holding and is a robust cine imaging technique in the presence of irregular heartbeats. It is a good alternative to the conventional breath-hold retro-gated cine for simplified acquisition and improved patient comfort. Real-time acquisition is achieved with the single-shot BSSFP readout without retro-gating. To maintain good temporal and spatial resolution, higher acceleration (e.g. &gt;4x parallel imaging) is required. As a result, the real-time cine images experience reduced signal-to-noise ratio (SNR), which limits its clinical acceptance.

Purpose

We developed a novel deep learning model architecture, the Convolutional Neural Network Transformer (CNNT), to improve the quality of real-time cine, under 4x, 5x and 6x acceleration.

Method

Convolutional Neural Networks (CNN) are widely used in CMR research to process cardiac images. Cardiac images are often acquired as a time series with strong inter-phase correlation. We combined the CNN with the more recent transformer model to develop a novel CNNT architecture. It takes in the entire 2D+T time series as input and has advantages of CNN for efficient computation and spatial invariance. It further inherits the advantages of attention layer in the transformer and is able to efficiently utilize the temporal correlation within a time series.

A CNNT model is developed to improve the SNR of real-time cine imaging. N=10 patients were scanned at a heart center, with 4x, 5x and 6x acceleration. Typical imaging parameters are: FOV 360×270mm2, flip angle 50°, acquired matrix size 160×90 for R=4 acceleration, 192×108 for R=5 and 6, temporal resolution 40ms for R=4, 42ms for R=5 and 35ms for R=6. The real-time images went through a TGRAPPA reconstruction [1] and the CNNT model. The SNR of TGRAPPA was measured with SNR units [2]. The Monte-Carlo pseudo-replica test was used to measure SNR for the CNNT model. For every cine series, two phases were picked for the end-systole and end-diastole. For every image picked, two region-of-interests were drawn in the myocardium and in the LV blood pool. The CNNT model was deployed inline on the MR scanner using the Gadgetron InlineAI [3].

Results

Figure 1 gives real-time cine images for three accelerations, reconstructed with TGRAPPA and CNNT. The parallel imaging TGRAPPA reconstruction suffers significant SNR loss from elevated g-factor and less acquired data. The deep learning CNNT model recovered SNR even at the very high 6x acceleration, without observed loss of boundary sharpness.

Table 1 lists the SNR measurement results. The TGRAPPA SNR decreased ∼4x from R=4 to R=6 for both the blood and myocardium. For the blood, the CNNT increased the SNR by 170%, 335%, 371% at R=4, 5 and 6. For the myocardium, the SNR increases were 335%, 634% and 828%.

Conclusion

We developed a convolutional neural network transformer model to recover the SNR for real-time cine imaging at higher acceleration.

Asymmetric septal thickening is observed in hypertrophic cardiomyopathy mutation carriers without left ventricular hypertrophy: insights from AI analysis

Funding Acknowledgements

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Dr Hughes is supported by the British Heart Foundation (grant number FS/17/82/33222).

Background

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiac disease characterised by left ventricular hypertrophy (LVH), often, with asymmetric septal thickening. Despite the prevalence of inherited mutations present in &gt;50% of cases, there is variable phenotypic expression in those with abnormal sarcomere protein genes. In individuals with abnormal genes but without LVH, we hypothesised that there is subtle asymmetric septal hypertrophy, detectable by the increased precision offered by an artificial intelligence (AI) tool for measuring wall thickness.

Purpose

We explored the septal-lateral ratio measured by AI in individuals with an identified genotype but no left ventricular hypertrophy as a component of sub-clinical HCM.

Methods

43 individuals with identified genotype, but no left ventricular hypertrophy (G+LVH-) and 97 age-, sex- and disease-matched controls underwent CMR. Patients were excluded if they had a maximum wall thickness (MWT) of ≥13mm. A clinically validated AI tool was used to measure the MWT, for each segment in the 16-segment AHA model. The septal-lateral ratio was calculated using the septal segment with the largest MWT and the lateral segment with the largest MWT.

Results

The mean septal-lateral ratio of the G+LVH- patients was 1.22 (SD 0.22) and the mean septal-lateral ratio of the matched controls was 1.14 (SD 0.15) with a statistically significant mean difference of 0.08 (p=0.01). There was no significant difference between the MWT of the G+LVH- patients at 10.3mm (SD 2.2) and healthy volunteers at 10.1mm (SD 1.8) (p = 0.61).

Conclusion

G+LVH- patients have a 7% increase in their septal-lateral ratio compared with age-matched controls despite the lack of difference in the MWT. Using increased precision offered by AI, early features of HCM can be observed in patients without overt LVH.

CNNT DB-LGE: free-breathing dark blood late enhancement imaging using the convolutional neural network transformer speeds acquisition by 50%

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Supported in part by the Division of Intramural Research of the National Heart, Lung, and Blood Institute, National Institutes of Health (grants Z1A-HL006214-05 and Z1A-HL006242-02).

Background

Dark blood late gadolinium enhancement (DB-LGE) imaging shows superior delineation of myocardial infarction (MI), especially at the sub-endocardial boundary. Our previous study [1] developed a free-breathing DB-LGE with the single shot SSFP readout, phase sensitive inversion recovery (PSIR) reconstruction, and respiratory motion corrected averaging. To compensate the potential signal-to-noise ratio loss, our previous DB-LGE doubled the measurements, thereby increasing the acquisition time.

Purpose

In this study, we developed a deep learning image enhancement model using a novel neural network architecture called the convolutional neural network transformer (CNNT) to improve the image quality of DB-LGE and to reduce the acquisition time by decreasing the number of measurements.

Methods

A novel image enhancement model was developed using a novel network architecture called the Convolutional Neural Network Transformer (CNNT) proposed by us. This architecture is suitable for the 2D+Time CMR acquisition, by exploiting the temporal correlation between images over multiple averages.

The evaluation was first retrospectively conducted on a cohort of 12 patients acquired with the original protocol [1] using the full 16 measurements. For every subject, a complete short-axis stack (typically 12 slices) was acquired to cover the entire left ventricular. The imaging data was reconstructed in three ways. Original: using all acquired 16 measurements. This is our base-line protocol. Original 50%: using only the first 8 measurements. CNNT 50%: using only the first 8 averages, but performing the CNNT deep learning image enhancement before MOCO PSIR reconstruction. Two experienced imaging researchers (PK and MF, &gt;10 years of experience for both) scored all DB-LGE images for the overall quality, diagnostic confidence and delineation of MI/boundaries (5 = excellent, 4 = good, 3 = fair, 2 = poor, and 1 = non-diagnostic). The CNNT DB-LGE was deployed to the MR scanner using the Gadgetron InlineAI [2].

Results

Figure 1 gives examples of DB-LGE with three reconstruction methods. The CNNT image has higher SNR and well delineated MI. The Original images with the longest acquisition have good quality and the Original-50% acquired with 8 measurements are good quality but have reduced SNR. The mean scores for overall image quality, diagnostic confidence and MI delineation of two reviewers were 4.88±0.23, 4.88±0.23, 4.83±0.25 for CNNT and 4.96±0.14, 4.96±0.14, 4.67±0.39 for the original approach. No significant differences were found between the original and the CNNT (P&gt;0.15 for all).

Figure 2 shows an acute MI patient prospectively acquired with the 50% scan time reduction, with and without the CNNT enhancement. The resulting PSIR images well delineate the MVO due to the acute MI, with improved SNR.

Conclusion

A novel CNNT model was proposed and evaluated to speed up the free-breathing MOCO DB LGE by 50% without sacrificing image quality.

Improving the diagnostic accuracy of apical hypertrophic cardiomyopathy using machine learning

Funding Acknowledgements

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Dr Hughes is supported by the British Heart Foundation (grant number FS/17/82/33222).

Introduction

The imaging criteria for diagnosis of apical hypertrophic cardiomyopathy (ApHCM) is a maximum wall thickness (MWT) ≥15mm. CMR enables detection of subtle phenotypic features (e.g. loss of apical tapering, cavity obliteration) and coupled with characteristic electrocardiogram changes, ApHCM can be diagnosed without overt hypertrophy. However, these patients are not captured by current diagnostic criteria.

Purpose

We sought to use a machine learning tool to quantify wall thickness and identify patients with ‘relative’ ApHCM that do not reach current diagnostic thresholds.

Methods

CMR images from 4118 healthy participants from the UK Biobank were segmented automatically with a clinically validated machine learning algorithm and wall thickness measured at all point in the myocardium by solving a solution to Laplace’s equation. MWT were pooled into 16 AHA segments and indexed to body surface area (BSA). The non-indexed and indexed segmental upper limit of normal was calculated as the mean + 3 standard deviations (the equivalent of 95% confidence interval after correcting for multiple [16] comparisons using the Bonferroni method).

Results

73 overt ApHCM subjects (MWT&gt;15mm) and 31 relative ApHCM subjects underwent CMR scanning. In healthy controls, the non-indexed (and indexed) upper limits were calculated for the apical-anterior 10.2mm (5.2 mm/m2), apical-septal 11.1mm (5.6 mm/m2), apical-inferior 10.5mm (5.3 mm/m2) and apical-lateral 10.1mm (5.2 mm/m2) segments. With a non-indexed cut-off, all (73 of 73) overt ApHCM and 84% (26 of 31) relative ApHCM were classified as having an abnormally thick apex. 3% (127 of 4118) of the healthy UK Biobank cohort were classified as abnormal, as expected. Using an indexed cut-off, all overt ApHCM and 87% (27/31) relative ApHCM were classified as abnormal, and 3% (123 of 4118) of the healthy UK Biobank cohort were misclassified.

Conclusion

We can successfully classify 87% of relative ApHCM patients from a normative reference range derived from a large cohort of healthy patients – a significant improvement on existing methods. We show that the specificity and sensitivity is increased when MWT is indexed to BSA. For practical clinical application, we recommend a cut-off of 10mm or an indexed cut-off of 5mm/m2 in any apical segment to diagnose apical LVH. Overt and relative apical HCM examplesHealthy controls AHA maps (non-indexed)

Speeding up cardiac MR segmentation with semi-supervision: applications in cine imaging

Funding Acknowledgements

Type of funding sources: Private company. Main funding source(s): This material is based upon work supported by the Google Cloud Research Credits program with the award GCP19980904.

Background

Segmentation is an important postprocessing step in cardiac magnetic resonance (CMR) imaging that enables quantitative assessment of functional parameters. Deep learning can automate the segmentation process, producing accurate contours of cardiac structures while reducing the time required to analyze images and the interobserver variation compared to manual analysis. However, common approaches to training neural networks (NNs) require large amounts of labeled data, which is costly to generate and slows down the development of CMR segmentation NNs for new applications. Semi-supervision is an approach to alleviate this labeling burden by relying on abundant unlabeled data and a smaller amount of labeled data to train NNs.

Purpose

We propose a novel semi-supervised method to train CMR segmentation NNs. We use the proposed method to train NNs to segment the left ventricle in CMR cine images. Ultimately, we aim to show that semi-supervision can drastically reduce the amount of labeled data required to develop machine learning segmentation applications for CMR while maintaining high performance.

Methods

Our dataset consists of 1,208 short-axis cine CMR images and 1,244 long-axis cine CMR images. An expert annotator manually segmented the endocardium on the end-diastolic and end-systolic short-axis and long-axis images and the epicardium on the end-diastolic short-axis images. We split the dataset randomly by patient into 60% training, 20% validation, and 20% testing data. We train semi-supervised segmentation networks using a supervised cross-entropy loss to learn from the labeled training data and a cosine embedding loss in addition to a pseudo-labeling step to learn from the unlabeled training data. To evaluate how performance changes with different amounts of labeled training data, we vary the percent of training data that has labels from &lt;1%-100%. We evaluate the predicted segmentation masks using the Dice coefficient.

Results

Using only 100 labeled image slices, the semi-supervised segmentation NNs achieve a mean Dice coefficient within 1.10% of networks trained with fully labeled training sets, corresponding to &gt;85% reduction in required labeled training data (Table 1). The proposed semi-supervised method improves performance over naïve training by 6.21% for the most limited labeled data setting (i.e., 10 labeled image slices; Figure 1).

Conclusion

We have shown that NNs trained with limited labeled data achieve high performance on left ventricle segmentation in short-axis and long-axis CMR cines. The proposed approach is flexible and broadly applicable to different CMR segmentation tasks, enabling rapid development of segmentation networks for many cardiac structures and applications. Table 1Figure 1

Search for an Excess of Electron Neutrino Interactions in MicroBooNE Using Multiple Final-State Topologies

We present a measurement of ν_{e} interactions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the MiniBooNE Collaboration. Three independent ν_{e} searches are performed across multiple single electron final states, including an exclusive search for two-body scattering events with a single proton, a semi-inclusive search for pionless events, and a fully inclusive search for events containing all hadronic final states. With differing signal topologies, statistics, backgrounds, reconstruction algorithms, and analysis approaches, the results are found to be either consistent with or modestly lower than the nominal ν_{e} rate expectations from the Booster Neutrino Beam and no excess of ν_{e} events is observed.

The COVANOS trial - insight into post-COVID olfactory dysfunction and the role of smell training

BACKGROUND

Olfactory dysfunction is a cardinal symptom of COVID-19 infection, however, studies assessing long-term olfactory dysfunction are limited and no randomised-controlled trials (RCTs) of early olfactory training have been conducted.

METHODOLOGY

We conducted a prospective, multi-centre study consisting of baseline psychophysical measurements of smell and taste function. Eligible participants were further recruited into a 12-week RCT of olfactory training versus control (safety information). Patient-reported outcomes were measured using an electronic survey and BSIT at baseline and 12 weeks. An additional 1-year follow-up was open to all participants.

RESULTS

218 individuals with a sudden loss of sense of smell of at least 4-weeks were recruited. Psychophysical smell loss was observed in only 32.1%; 63 participants were recruited into the RCT. The absolute difference in BSIT improvement after 12 weeks was 0.45 higher in the intervention arm. 76 participants completed 1-year follow-up; 10/19 (52.6%) of participants with an abnormal baseline BSIT test scored below the normal threshold at 1-year, and 24/29 (82.8%) had persistent parosmia.

CONCLUSIONS

Early olfactory training may be helpful, although our findings are inconclusive. Notably, a number of individuals who completed the 1-year assessment had persistent smell loss and parosmia at 1-year. As such, both should be considered important entities of long-Covid and further studies to improve management are highly warranted.

Advanced electrocardiography predicts early cardiac involvement and incident arrhythmias in Fabry disease

Funding Acknowledgements

Type of funding sources: None.

Background

Fabry disease is an X-linked disorder, with cardiovascular involvement characterised by progressive myocardial sphingolipid deposition. Cardiac disease is a major contributor to morbidity and mortality. Cardiac magnetic resonance (CMR) with T1 mapping and advanced electrocardiography (A-ECG) offer both diagnostic and prognostic potential.

Purpose

To evaluate the predictive power of A-ECG markers in identifying: 1) early cardiac involvement defined as low myocardial T1 on CMR, and 2) adverse cardiovascular outcomes defined as any arrhythmia requiring therapy, atrial fibrillation, hospitalisation for heart failure or mortality.

Methods

Patients included in this longitudinal, multi-centre study underwent same-day standard resting 12-lead ECG and CMR. CMR included standard cine imaging, T1 mapping with modified Look Locker inversion recovery (MOLLI, 5s(3s)3s), and late gadolinium enhancement (LGE). ECG digital files were analysed using in-house A-ECG software. A-ECG analysis included conventional ECG measures, derived vectorcardiographic measures, and singular value decomposition measures of waveform complexity. Significant A-ECG variables were identified using stepwise forward regression and incorporated in a multivariable logistic regression A-ECG score. A Youden index was applied to identify best threshold score and bootstrapping performed to calculate the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and 95% confidence intervals (CI).

Results

Among included patients (n=155, 40% male, age 46±14 years, 39% on enzyme replacement therapy), left ventricular mass index was higher in males compared to females (106 vs. 59 g/m2, p&lt;0.001), 80% of patients had myocardial native T1 below the local reference range (933 vs. 968 ms, p=0.06), and 51% (70/136) had focal LGE. Multivariable A-ECG scores for detecting low T1, any arrhythmia, or atrial fibrillation had an AUC [95%CI], sensitivity, and specificity of 0.82 [0.75-0.89], 72 [55-95]%, 85 [66-71]%; 0.89 [0.82-0.95], 82 [68-94]%, 88 [70-96]%; and 0.89 [0.80-0.96], 92 [77-100]%, 83 [76-92]%, respectively, Figure 1. No predictors of heart failure hospitalisation or mortality were found.

Conclusion

A-ECG analysis of the resting 12-lead ECG has good diagnostic performance for predicting early myocardial involvement and the occurrence of arrhythmias in Fabry disease. This supports the use of A-ECG both as a screening tool to diagnose early cardiac disease, and for identifying those at risk of adverse arrhythmic outcomes.