Mitral regurgitation quantified by CMR 4D-flow is associated with microvascular obstruction post reperfused ST-segment elevation myocardial infarction

Objectives

Mitral regurgitation (MR) and microvascular obstruction (MVO) are common complications of myocardial infarction (MI). This study aimed to investigate the association between MR in ST-elevation MI (STEMI) subjects with MVO post-reperfusion. STEMI subjects undergoing primary percutaneous intervention were enrolled. Cardiovascular magnetic resonance (CMR) imaging was performed within 48-hours of initial presentation. 4D flow images of CMR were analysed using a retrospective valve tracking technique to quantify MR volume, and late gadolinium enhancement images of CMR to assess MVO.

Results

Among 69 patients in the study cohort, 41 had MVO (59%). Patients with MVO had lower left ventricular (LV) ejection fraction (EF) (42 ± 10% vs. 52 ± 8%, P < 0.01), higher end-systolic volume (98 ± 49 ml vs. 73 ± 28 ml, P < 0.001) and larger scar volume (26 ± 19% vs. 11 ± 9%, P < 0.001). Extent of MVO was associated with the degree of MR quantified by 4D flow (R = 0.54, P = 0.0003). In uni-variate regression analysis, investigating the association of CMR variables to the degree of acute MR, only the extent of MVO was associated (coefficient = 0.27, P = 0.001). The area under the curve for the presence of MVO was 0.66 (P = 0.01) for MR > 2.5 ml. We conclude that in patients with reperfused STEMI, the degree of acute MR is associated with the degree of MVO.

Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension

Aims

Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning.

Methods and results

Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality.

Conclusion

The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH.

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements

BACKGROUND

Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value.

METHODS

A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018).

RESULTS

All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm2, 91.2 ± 4.5 cm2 and 93.2 ± 3.2 cm2, respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm2, 87.0 ± 5.8 cm2 and 91.8 ± 4.8 cm2. Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84.

CONCLUSION

Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality.

Severe pulmonary hypertension associated with lung disease is characterised by a loss of small pulmonary vessels on quantitative computed tomography

Background

Pulmonary hypertension (PH) in patients with chronic lung disease (CLD) predicts reduced functional status, clinical worsening and increased mortality, with patients with severe PH-CLD (≥35 mmHg) having a significantly worse prognosis than mild to moderate PH-CLD (21-34 mmHg). The aim of this cross-sectional study was to assess the association between computed tomography (CT)-derived quantitative pulmonary vessel volume, PH severity and disease aetiology in CLD.

Methods

Treatment-naïve patients with CLD who underwent CT pulmonary angiography, lung function testing and right heart catheterisation were identified from the ASPIRE registry between October 2012 and July 2018. Quantitative assessments of total pulmonary vessel and small pulmonary vessel volume were performed.

Results

90 patients had PH-CLD including 44 associated with COPD/emphysema and 46 with interstitial lung disease (ILD). Patients with severe PH-CLD (n=40) had lower small pulmonary vessel volume compared to patients with mild to moderate PH-CLD (n=50). Patients with PH-ILD had significantly reduced small pulmonary blood vessel volume, compared to PH-COPD/emphysema. Higher mortality was identified in patients with lower small pulmonary vessel volume.

Conclusion

Patients with severe PH-CLD, regardless of aetiology, have lower small pulmonary vessel volume compared to patients with mild-moderate PH-CLD, and this is associated with a higher mortality. Whether pulmonary vessel changes quantified by CT are a marker of remodelling of the distal pulmonary vasculature requires further study.

Lung MRI with hyperpolarised gases: current & future clinical perspectives

The use of pulmonary MRI in a clinical setting has historically been limited. Whilst CT remains the gold-standard for structural lung imaging in many clinical indications, technical developments in ultrashort and zero echo time MRI techniques are beginning to help realise non-ionising structural imaging in certain lung disorders. In this invited review, we discuss a complementary technique - hyperpolarised (HP) gas MRI with inhaled ³He and ¹²⁹Xe - a method for functional and microstructural imaging of the lung that has great potential as a clinical tool for early detection and improved understanding of pathophysiology in many lung diseases. HP gas MRI now has the potential to make an impact on clinical management by enabling safe, sensitive monitoring of disease progression and response to therapy. With reference to the significant evidence base gathered over the last two decades, we review HP gas MRI studies in patients with a range of pulmonary disorders, including COPD/emphysema, asthma, cystic fibrosis, and interstitial lung disease. We provide several examples of our experience in Sheffield of using these techniques in a diagnostic clinical setting in challenging adult and paediatric lung diseases.

Integrated Cardiopulmonary MRI Assessment of Pulmonary Hypertension

Pulmonary hypertension (PH) is a heterogeneous condition that can affect the lung parenchyma, pulmonary vasculature, and cardiac chambers. Accurate diagnosis often requires multiple complex assessments of the cardiac and pulmonary systems. MRI is able to comprehensively assess cardiac structure and function, as well as lung parenchymal, pulmonary vascular, and functional lung changes. Therefore, MRI has the potential to provide an integrated functional and structural assessment of the cardiopulmonary system in a single exam. Cardiac MRI is used in the assessment of PH in most large PH centers, whereas lung MRI is an emerging technique in patients with PH. This article reviews the current literature on cardiopulmonary MRI in PH, including cine MRI, black-blood imaging, late gadolinium enhancement, T₁ mapping, myocardial strain analysis, contrast-enhanced perfusion imaging and contrast-enhanced MR angiography, and hyperpolarized gas functional lung imaging. This article also highlights recent developments in this field and areas of interest for future research including cardiac MRI-based diagnostic models, machine learning in cardiac MRI, oxygen-enhanced ¹ H imaging, contrast-free ¹ H perfusion and ventilation imaging, contrast-free angiography and UTE imaging. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.

Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions

Background: The reproducibility of mitral regurgitation (MR) quantification by cardiovascular magnetic resonance (CMR) imaging using different software solutions remains unclear. This research aimed to investigate the reproducibility of MR quantification between two software solutions: MASS (version 2019 EXP, LUMC, Netherlands) and CAAS (version 5.2, Pie Medical Imaging). Methods: CMR data of 35 patients with MR (12 primary MR, 13 mitral valve repair/replacement, and ten secondary MR) was used. Four methods of MR volume quantification were studied, including two 4D-flow CMR methods (MRMVAV and MRJet) and two non-4D-flow techniques (MRStandard and MRLVRV). We conducted within-software and inter-software correlation and agreement analyses. Results: All methods demonstrated significant correlation between the two software solutions: MRStandard (r=0.92, p<0.001), MRLVRV (r=0.95, p<0.001), MRJet (r=0.86, p<0.001), and MRMVAV (r=0.91, p<0.001). Between CAAS and MASS, MRJet and MRMVAV, compared to each of the four methods, were the only methods not to be associated with significant bias. Conclusions: We conclude that 4D-flow CMR methods demonstrate equivalent reproducibility to non-4D-flow methods but greater levels of agreement between software solutions.

CMR Measures of Left Atrial Volume Index and Right Ventricular Function Have Prognostic Value in Chronic Thromboembolic Pulmonary Hypertension

Providing prognostic information is important when counseling patients and planning treatment strategies in chronic thromboembolic pulmonary hypertension (CTEPH). The aim of this study was to assess the prognostic value of gold standard imaging of cardiac structure and function using cardiac magnetic resonance imaging (CMR) in CTEPH. Consecutive treatment-naive patients with CTEPH who underwent right heart catheterization and CMR between 2011 and 2017 were identified from the ASPIRE (Assessing-the-Specturm-of-Pulmonary-hypertensIon-at-a-REferral-center) registry. CMR metrics were corrected for age and sex where appropriate. Univariate and multivariate regression models were generated to assess the prognostic ability of CMR metrics in CTEPH. Three hundred and seventy-five patients (mean+/-standard deviation: age 64+/-14 years, 49% female) were identified and 181 (48%) had pulmonary endarterectomy (PEA). For all patients with CTEPH, left-ventricular-stroke-volume-index-%predicted (LVSVI%predicted) (p = 0.040), left-atrial-volume-index (LAVI) (p = 0.030), the presence of comorbidities, incremental shuttle walking test distance (ISWD), mixed venous oxygen saturation and undergoing PEA were independent predictors of mortality at multivariate analysis. In patients undergoing PEA, LAVI (p < 0.010), ISWD and comorbidities and in patients not undergoing surgery, right-ventricular-ejection-fraction-%predicted (RVEF%pred) (p = 0.040), age and ISWD were independent predictors of mortality. CMR metrics reflecting cardiac function and left heart disease have prognostic value in CTEPH. In those undergoing PEA, LAVI predicts outcome whereas in patients not undergoing PEA RVEF%pred predicts outcome. This study highlights the prognostic value of imaging cardiac structure and function in CTEPH and the importance of considering left heart disease in patients considered for PEA.

Computed tomography lung parenchymal descriptions in routine radiological reporting have diagnostic and prognostic utility in patients with idiopathic pulmonary arterial hypertension and pulmonary hypertension associated with lung disease

BACKGROUND

Patients with pulmonary hypertension (PH) and lung disease may pose a diagnostic dilemma between idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-CLD). The prognostic impact of common computed tomography (CT) parenchymal features is unknown.

METHODS

660 IPAH and PH-CLD patients assessed between 2001 and 2019 were included. Reports for all CT scans 1 year prior to diagnosis were analysed for common lung parenchymal patterns. Cox regression and Kaplan-Meier analysis were performed.

RESULTS

At univariate analysis of the whole cohort, centrilobular ground-glass (CGG) changes (hazard ratio, HR 0.29) and ground-glass opacification (HR 0.53) predicted improved survival, while honeycombing (HR 2.79), emphysema (HR 2.09) and fibrosis (HR 2.38) predicted worse survival (all p<0.001). Fibrosis was an independent predictor after adjusting for baseline demographics, PH severity and diffusing capacity of the lung for carbon monoxide (HR 1.37, p<0.05). Patients with a clinical diagnosis of IPAH who had an absence of reported parenchymal lung disease (IPAH-noLD) demonstrated superior survival to patients diagnosed with either IPAH who had coexistent CT lung disease or PH-CLD (2-year survival of 85%, 60% and 46%, respectively, p<0.05). CGG changes were present in 23.3% of IPAH-noLD and 5.8% of PH-CLD patients. There was no significant difference in survival between IPAH-noLD patients with or without CGG changes. PH-CLD patients with fibrosis had worse survival than those with emphysema.

INTERPRETATION

Routine clinical reports of CT lung parenchymal disease identify groups of patients with IPAH and PH-CLD with significantly different prognoses. Isolated CGG changes are not uncommon in IPAH but are not associated with worse survival.

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning

Introduction

Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.

Methods

A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort.

Results

Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases.

Conclusion

Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.

Imaging and Risk Stratification in Pulmonary Arterial Hypertension: Time to Include Right Ventricular Assessment

Background

Current European Society of Cardiology and European Respiratory Society guidelines recommend regular risk stratification with an aim of treating patients with pulmonary arterial hypertension (PAH) to improve or maintain low-risk status (<5% 1-year mortality).

Methods

Consecutive patients with PAH who underwent cardiac magnetic resonance imaging (cMRI) were identified from the Assessing the Spectrum of Pulmonary hypertension Identified at a Referral centre (ASPIRE) registry. Kaplan-Meier survival curves, locally weighted scatterplot smoothing regression and multi-variable logistic regression analysis were performed.

Results

In 311 consecutive, treatment-naïve patients with PAH undergoing cMRI including 121 undergoing follow-up cMRI, measures of right ventricular (RV) function including right ventricular ejection fraction (RVEF) and RV end systolic volume and right atrial (RA) area had prognostic value. However, only RV metrics were able to identify a low-risk status. Age (p < 0.01) and RVEF (p < 0.01) but not RA area were independent predictors of 1-year mortality.

Conclusion

This study highlights the need for guidelines to include measures of RV function rather than RA area alone to aid the risk stratification of patients with PAH.

DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the healthcare system. Along with COVID-19, other etiologies of pneumonia and Tuberculosis (TB) constitute additional challenges to the medical system. Pneumonia (viral as well as bacterial) kills about 2 million infants every year and is consistently estimated as one of the most important factor of childhood mortality (according to the World Health Organization). Chest X-ray (CXR) and computed tomography (CT) scans are the primary imaging modalities for diagnosing respiratory diseases. Although CT scans are the gold standard, they are more expensive, time consuming, and are associated with a small but significant dose of radiation. Hence, CXR have become more widespread as a first line investigation. In this regard, the objective of this work is to develop a new deep transfer learning pipeline, named DenResCov-19, to diagnose patients with COVID-19, pneumonia, TB or healthy based on CXR images. The pipeline consists of the existing DenseNet-121 and the ResNet-50 networks. Since the DenseNet and ResNet have orthogonal performances in some instances, in the proposed model we have created an extra layer with convolutional neural network (CNN) blocks to join these two models together to establish superior performance as compared to the two individual networks. This strategy can be applied universally in cases where two competing networks are observed. We have tested the performance of our proposed network on two-class (pneumonia and healthy), three-class (COVID-19 positive, healthy, and pneumonia), as well as four-class (COVID-19 positive, healthy, TB, and pneumonia) classification problems. We have validated that our proposed network has been able to successfully classify these lung-diseases on our four datasets and this is one of our novel findings. In particular, the AUC-ROC are 99.60, 96.51, 93.70, 96.40% and the F1 values are 98.21, 87.29, 76.09, 83.17% on our Dataset X-Ray 1, 2, 3, and 4 (DXR1, DXR2, DXR3, DXR4), respectively.

Myocardial inflammation and energetics by cardiac MRI: a review of emerging techniques

The role of inflammation in cardiovascular pathophysiology has gained a lot of research interest in recent years. Cardiovascular Magnetic Resonance has been a powerful tool in the non-invasive assessment of inflammation in several conditions. More recently, Ultrasmall superparamagnetic particles of iron oxide have been successfully used to evaluate macrophage activity and subsequently inflammation on a cellular level. Current evidence from research studies provides encouraging data and confirms that this evolving method can potentially have a huge impact on clinical practice as it can be used in the diagnosis and management of very common conditions such as coronary artery disease, ischaemic and non-ischaemic cardiomyopathy, myocarditis and atherosclerosis. Another important emerging concept is that of myocardial energetics. With the use of phosphorus magnetic resonance spectroscopy, myocardial energetic compromise has been proved to be an important feature in the pathophysiological process of several conditions including diabetic cardiomyopathy, inherited cardiomyopathies, valvular heart disease and cardiac transplant rejection. This unique tool is therefore being utilized to assess metabolic alterations in a wide range of cardiovascular diseases. This review systematically examines these state-of-the-art methods in detail and provides an insight into the mechanisms of action and the clinical implications of their use.

The REPAIR Study: Effects of Macitentan on RV Structure and Function in Pulmonary Arterial Hypertension

OBJECTIVES

The REPAIR (Right vEntricular remodeling in Pulmonary ArterIal hypeRtension) study evaluated the effect of macitentan on right ventricular (RV) and hemodynamic outcomes in patients with pulmonary arterial hypertension (PAH), using cardiac magnetic resonance (CMR) and right heart catheterization (RHC).

BACKGROUND

RV failure is the primary cause of death in PAH. CMR is regarded as the most accurate noninvasive method for assessing RV function and remodeling and CMR measures of RV function and structure are strongly prognostic for survival in patients with PAH. Despite this, CMR is not routinely used in PAH clinical trials.

METHODS

REPAIR was a 52-week, open-label, single-arm, multicenter, phase 4 study evaluating the effect of macitentan 10 mg, with or without phosphodiesterase type-5 inhibition, on RV remodeling and function and cardiopulmonary hemodynamics. Primary endpoints were change from baseline to week 26 in RV stroke volume, determined by CMR; and pulmonary vascular resistance, determined by RHC. Efficacy measures were assessed for all patients with baseline and week 26 data for both primary endpoints.

RESULTS

At a prespecified interim analysis in 42 patients, both primary endpoints were met, enrollment was stopped, and the study was declared positive. At final analysis (n = 71), RV stroke volume increased by 12 mL (96% confidence level: 8.4-15.6 mL; P < 0.0001) and pulmonary vascular resistance decreased by 38% (99% confidence level: 31%-44%; P < 0.0001) at week 26. Significant positive changes were also observed in secondary and exploratory CMR (RV and left ventricular), hemodynamic, and functional endpoints at week 26. Improvements in CMR RV and left ventricular variables and functional parameters were maintained at week 52. Safety (n = 87) was consistent with previous clinical trials.

CONCLUSIONS

In the context of this study, macitentan treatment in patients with PAH resulted in significant and clinically-relevant improvements in RV function and structure and cardiopulmonary hemodynamics. At 52 weeks, improvements in RV function and structure were sustained. (REPAIR: Right vEntricular remodeling in Pulmonary ArterIal hypeRtension [REPAIR]; NCT02310672).

Thermal Analysis of Benzotriazolium Perrhenate and Its Implication to Rhenium Metal

The thermal analysis behavior of C₆H₆N₃[ReO₄] was studied by simultaneous thermal gravimetric analysis (TGA) and differential scanning calorimetry (DSC) up to 700 °C under argon. Such analysis afforded rhenium metal, which was characterized by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX) spectroscopy. XRD peak broadening due to crystallite size and lattice strain was analyzed by both Williamson-Hall (W-H) and Debye-Scherrer (D-S) methods. Efforts to isolate Re metal from the thermal treatment of benzotriazole (BTA = C₆H₅N₃) with NH₄ReO₄ and Re₂O₇ under various atmospheres and temperatures are also reported. The results provide a significant insight into the chemistry of group VII transition metals, investigate the potential use of benzotriazole as a reducing agent for metal productions, and demonstrate a successful convenient method for rhenium metal production, which could be applied to other refractory metals.

Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions

Background: The reproducibility of mitral regurgitation (MR) quantification by cardiovascular magnetic resonance (CMR) imaging using different software solutions remains unclear. This research aimed to investigate the reproducibility of MR quantification between two software solutions: MASS (version 2019 EXP, LUMC, Netherlands) and CAAS (version 5.2, Pie Medical Imaging). Methods: CMR data of 35 patients with MR (12 primary MR, 13 mitral valve repair/replacement, and ten secondary MR) was used. Four methods of MR volume quantification were studied, including two 4D-flow CMR methods (MRMVAV and MRJet) and two non-4D-flow techniques (MRStandard and MRLVRV). We conducted within-software and inter-software correlation and agreement analyses. Results: All methods demonstrated significant correlation between the two software solutions: MRStandard (r=0.92, p<0.001), MRLVRV (r=0.95, p<0.001), MRJet (r=0.86, p<0.001), and MRMVAV (r=0.91, p<0.001). Between CAAS and MASS, MRJet and MRMVAV, compared to each of the four methods, were the only methods not to be associated with significant bias. Conclusions: We conclude that 4D-flow CMR methods demonstrate equivalent reproducibility to non-4D-flow methods but greater levels of agreement between software solutions.

Xenon ventilation MRI in difficult asthma: initial experience in a clinical setting

Background

Hyperpolarised gas magnetic resonance imaging (MRI) can be used to assess ventilation patterns. Previous studies have shown the image-derived metric of ventilation defect per cent (VDP) to correlate with forced expiratory volume in 1 s (FEV₁)/forced vital capacity (FVC) and FEV₁ in asthma.

Objectives

The aim of this study was to explore the utility of hyperpolarised xenon-129 (¹²⁹Xe) ventilation MRI in clinical care and examine its relationship with spirometry and other clinical metrics in people seen in a severe asthma service.

Methods

26 people referred from a severe asthma clinic for MRI scanning were assessed by contemporaneous ¹²⁹Xe MRI and spirometry. A subgroup of 18 patients also underwent reversibility testing with spirometry and MRI. Quantitative MRI measures of ventilation were calculated, VDP and the ventilation heterogeneity index (VH_I), and compared to spirometry, Asthma Control Questionnaire 7 (ACQ7) and blood eosinophil count. Images were reviewed by a multidisciplinary team.

Results

VDP and VH_I correlated with FEV₁, FEV₁/FVC and forced expiratory flow between 25% and 75% of FVC but not with ACQ7 or blood eosinophil count. Discordance of MRI imaging and symptoms and/or pulmonary function tests also occurred, prompting diagnostic re-evaluation in some cases.

Conclusion

Hyperpolarised gas MRI provides a complementary method of assessment in people with difficult to manage asthma in a clinical setting. When used as a tool supporting clinical care in a severe asthma service, occurrences of discordance between symptoms, spirometry and MRI scanning indicate how MRI scanning may add to a management pathway.

This article demonstrates the feasibility of using ¹²⁹Xe MRI in clinical practice. Discordance between symptoms, spirometry and MRI can support the use of further treatment or suggest coexisting breathing control issues or laryngeal disorders.https://bit.ly/3ky4oXP

Unenhanced computed tomography as a diagnostic tool in suspected pulmonary hypertension: a retrospective cross-sectional pilot study

Background: Computed tomography pulmonary angiography (CTPA) has been proposed to be diagnostic for pulmonary hypertension (PH) in multiple studies. However, the utility of the unenhanced CT measurements diagnosing PH has not been fully assessed.  This study aimed to assess the diagnostic utility and reproducibility of cardiac and great vessel parameters on unenhanced computed tomography (CT) in suspected pulmonary hypertension (PH). Methods: In total, 42 patients with suspected PH who underwent unenhanced CT thorax and right heart catheterization (RHC) were included in the study. Three observers (a consultant radiologist, a specialist registrar in radiology, and a medical student) measured the parameters by using unenhanced CT. Diagnostic accuracy of the parameters was assessed by area under the receiver operating characteristic curve (AUC). Inter-observer variability between the consultant radiologist (primary observer) and the two secondary observers was determined by intra-class correlation analysis (ICC). Results: Overall, 35 patients were diagnosed with PH by RHC while 7 patients were not. Main pulmonary arterial (MPA) diameter was the strongest (AUC 0.79 to 0.87) and the most reproducible great vessel parameter. ICC comparing the MPA diameter measurement of the consultant radiologist to the specialist registrar’s and the medical student’s were 0.96 and 0.92, respectively. Right atrial area was the cardiac measurement with highest accuracy and reproducibility (AUC 0.76 to 0.79; ICC 0.980, 0.950) followed by tricuspid annulus diameter (AUC 0.76 to 0.79; ICC 0.790, 0.800). Conclusions: MPA diameter and right atrial areas showed high reproducibility. Diagnostic accuracies of these were within the range of acceptable to excellent, and might have clinical value. Tricuspid annular diameter was less reliable and less diagnostic and was therefore not a recommended diagnostic measurement.

Reproducibility of left ventricular blood flow kinetic energy measured by four-dimensional flow CMR

OBJECTIVES

Four-dimensional flow CMR allows for a comprehensive assessment of the blood flow kinetic energy of the ventricles of the heart. In comparison to standard two-dimensional image acquisition, 4D flow CMR is felt to offer superior reproducibility, which is important when repeated examinations may be required. The objective was to evaluate the inter-observer and intra-observer reproducibility of blood flow kinetic energy assessment using 4D flow of the left ventricle in 20 healthy volunteers across two centres in the United Kingdom and the Netherlands.

DATA DESCRIPTION

This dataset contains 4D flow CMR blood flow kinetic energy data for 20 healthy volunteers with no known cardiovascular disease. Presented is kinetic energy data for the entire cardiac cycle (global), the systolic and diastolic components, in addition to blood flow kinetic energy for both early and late diastolic filling. This data is available for reuse and would be valuable in supporting other research, such as allowing for larger sample sizes with more statistical power for further analysis of these variables.

High interstudy repeatability of automatic deep learnt biventricular CMR measurements

Funding Acknowledgements

Type of funding sources: Foundation. Main funding source(s): Wellcome Trust (UK), NIHR (UK)

Introduction

Cardiac magnetic resonance (CMR) measurements have significant diagnostic and prognostic value. Accurate and repeatable measurements are essential to assess disease severity, evaluate therapy response and monitor disease progression. Deep learning approaches have shown promise for automatic left ventricular (LV) segmentation on CMR, however fully automatic right ventricular (RV) segmentation remains challenging. We aimed to develop a biventricular automatic contouring model and evaluate the interstudy repeatability of the model in a prospectively recruited cohort.

Methods

A deep learning CMR contouring model was developed in a retrospective multi-vendor (Siemens and General Electric), multi-pathology cohort of patients, predominantly with heart failure, pulmonary hypertension and lung diseases (n = 400, ASPIRE registry). Biventricular segmentations were made on all CMR studies across cardiac phases. To test the accuracy of the automatic segmentation, 30 ASPIRE CMRs were segmented independently by two CMR experts. Each segmentation was compared to the automatic contouring with agreement assessed using the Dice similarity coefficient (DSC). 

A prospective validation cohort of 46 subjects (10 healthy volunteers and 36 patients with pulmonary hypertension) were recruited to assess interstudy agreement of automatic and manual CMR assessments. Two CMR studies were performed during separate sessions on the same day. Interstudy repeatability was assessed using intraclass correlation coefficient (ICC) and Bland-Altman plots. 

Results

DSC showed high agreement (figure 1) comparing automatic and expert CMR readers, with minimal bias towards either CMR expert. The scan-scan repeatability CMR measurements were higher for all automatic RV measurements (ICC 0.89 to 0.98) compared to manual RV measurements (0.78 to 0.98). LV automatic and manual measurements were similarly repeatable (figure 2). Bland-Altman plots showed strong agreement with small mean differences between the scan-scan measurements (figure 2).

Conclusion

Fully automatic biventricular short-axis segmentations are comparable with expert manual segmentations, and have shown excellent interstudy repeatability.

Fully automated CMR derived stroke volume correlates with right heart catheter measurements in patients with suspected pulmonary hypertension

Funding Acknowledgements

Type of funding sources: Foundation. Main funding source(s): Welcome Trust (UK), NIHR (UK)

Introduction

Cardiac magnetic resonance (CMR) assessment plays a significant role in the diagnosis, prognosis and monitoring of patients with pulmonary hypertension (PH). We developed a deep learning model to automatically generate biventricular contours and validated its result in a prospective cohort of patients with suspected PH who underwent right heart catheterization (RHC).

Methods

A deep learning CMR contouring model was developed in a retrospective multi-vendor (Siemens and General Electric), multi-pathology cohort of patients, predominantly with heart failure, lung disease and PH (n = 400, ASPIRE registry). Biventricular segmentations were made on all CMR studies across cardiac phases. A prospective validation cohort of 102 suspected PH patients was recruited and they had RHC within 24 hours  of the CMR. To test the accuracy of the automatic segmentation, the RHC-thermodilution and CMR-derived measures of stroke volume (SV) were compared for manual and automated measurements. 

Results

The mean and standard deviation for the derived SV was 59 ml ± 21 measured by RHC and 75 ml ± 25 for automated and 79 ml ± 26 for manual CMR measurements. Automatic and manual CMR measurement correlated strongly with RHC derived SV; 0.73, 95% CI [0.62, 0.81] and 0.78, 95% CI [0.69, 0.85], respectively (figure 1).  The agreement between automatic and manual SV was high; interclass correlation coefficient (ICC) = 0.88, 95% CI [0.83, 0.92] and Bland-Altman plots showed a narrow spread of mean differences between manual and automatic measurements (figure 2).

Conclusion

In a prospective cohort, fully automatic CMR assessments corresponded accurately to invasive hemodynamics performed within 24 hours of a CMR study.

Clinical Translation of Three-Dimensional Scar, Diffusion Tensor Imaging, Four-Dimensional Flow, and Quantitative Perfusion in Cardiac MRI: A Comprehensive Review

Cardiovascular magnetic resonance (CMR) imaging is a versatile tool that has established itself as the reference method for functional assessment and tissue characterisation. CMR helps to diagnose, monitor disease course and sub-phenotype disease states. Several emerging CMR methods have the potential to offer a personalised medicine approach to treatment. CMR tissue characterisation is used to assess myocardial oedema, inflammation or thrombus in various disease conditions. CMR derived scar maps have the potential to inform ablation therapy—both in atrial and ventricular arrhythmias. Quantitative CMR is pushing boundaries with motion corrections in tissue characterisation and first-pass perfusion. Advanced tissue characterisation by imaging the myocardial fibre orientation using diffusion tensor imaging (DTI), has also demonstrated novel insights in patients with cardiomyopathies. Enhanced flow assessment using four-dimensional flow (4D flow) CMR, where time is the fourth dimension, allows quantification of transvalvular flow to a high degree of accuracy for all four-valves within the same cardiac cycle. This review discusses these emerging methods and others in detail and gives the reader a foresight of how CMR will evolve into a powerful clinical tool in offering a precision medicine approach to treatment, diagnosis, and detection of disease.

Feasibility and validation of trans-valvular flow derived by four-dimensional flow cardiovascular magnetic resonance imaging in patients with atrial fibrillation

Background: Four-dimensional (4D) flow cardiovascular magnetic resonance imaging (MRI) is an emerging technique used for intra-cardiac blood flow assessment. The role of 4D flow cardiovascular MRI in the assessment of trans-valvular flow in patients with atrial fibrillation (AF) has not previously been assessed. The purpose of this study was to assess the feasibility, image quality, and internal validity of 4D flow cardiovascular MRI in the quantification of trans-valvular flow in patients with AF. Methods: Patients with AF and healthy controls in sinus rhythm underwent cardiovascular MRI, including 4D flow studies. Quality assurance checks were done on the raw data and streamlines. Consistency was investigated by trans-valvular flow assessment between the mitral valve (MV) and the aortic valve (AV). Results: Eight patients with AF (88% male, mean age 62±13 years, mean heart rate (HR) 83±16 beats per minute (bpm)) were included and compared with ten healthy controls (70% male, mean age 41±20 years, mean HR 68.5±9 bpm). All scans were of either good quality with minimal blurring artefacts, or excellent quality with no artefacts. No significant bias was observed between the AV and MV stroke volumes in either healthy controls (-4.8, 95% CI -15.64 to 6.04; P=0.34) or in patients with AF (1.64, 95% CI -4.7 to 7.94; P=0.56). A significant correlation was demonstrated between MV and AV stroke volumes in both healthy controls (r=0.87, 95% CI 0.52 to 0.97; P=0.001) and in AF patients (r=0.82, 95% CI 0.26 to 0.97; P=0.01). Conclusions: In patients with AF, 4D flow cardiovascular MRI is feasible with good image quality, allowing for quantification of trans-valvular flow.