Role of cardiac MRI in the prediction of pre-Fontan end-diastolic ventricular pressure

Abstract Background: Growing evidence has emphasised the importance of ventricular performance in functionally single-ventricle patients, particularly concerning diastolic function. Cardiac MRI has been proposed as non-invasive alternative to pre-Fontan cardiac catheterisation in selected patients. Aim of the study: To identify clinical and cardiac magnetic resonance predictors of high pre-Fontan end-diastolic ventricular pressure. Method: In a retrospective single-centre study, 38 patients with functionally univentricular heart candidate for Fontan intervention, who underwent pre-Fontan cardiac catheterisation, beside a comprehensive cardiac MRI, echocardiographic, and clinical assessment were included. Medical and surgical history, cardiac magnetic resonance, cardiac catheterisation, echocardiographic, and clinical data were recorded. We investigated the association between non-invasive parameters and cardiac catheterisation pre-Fontan risk factors, in particular with end-diastolic ventricular pressure. Moreover, the impact of conventional invasive pre-Fontan risk factor on post-operative outcome as also assessed. Results: Post-operative complications were associated with higher end-diastolic ventricular pressure and Mayo Clinic indexes (p < 0.01 and p = 0.05, respectively). At receiver operating characteristic curve analysis end-diastolic ventricular pressure ≥ 10.5 mmHg predicted post-operative complications with a sensitivity of 75% and specificity of 88% (AUC: 0.795, 95% CI 0.576;1.000, p < 0.05). At multivariate analysis, both systemic right ventricle (OR: 23.312, 95% CI: 2.704–200.979, p < 0.01) and superior caval vein indexed flow (OR: 0.996, 95% CI: 0.993–0.999, p < 0.05) influenced end-diastolic ventricular pressure ≥ 10.5 mmHg. Conclusions: A reduced superior caval vein flow, evaluated at cardiac magnetic resonance, is associated with higher end-diastolic ventricular pressure a predictor of early adverse outcome in post-Fontan patients.

Download Full-text

333Regional myocardial work by cardiac magnetic resonance and non-invasive left ventricular pressure: a feasibility study in left bundle branch block

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jez122.002 ◽

2019 ◽

Vol 20 (Supplement_2) ◽

Cited By ~ 1

Author(s):

C Kjellstad Larsen ◽

J M Aalen ◽

C Stokke ◽

J G Fjeld ◽

E Kongsgaard ◽

...

Keyword(s):

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Feasibility Study ◽

Left Bundle Branch Block ◽

Left Ventricular Pressure ◽

Left Ventricular ◽

Ventricular Pressure ◽

Bundle Branch Block ◽

Non Invasive

Download Full-text

Endomyocardial fibrosis and mural thrombus in a 4-year-old girl due to idiopathic hypereosinophilia syndrome described with serial cardiac magnetic resonance imaging

Cardiology in the Young ◽

10.1017/s1047951115000013 ◽

2015 ◽

Vol 26 (1) ◽

pp. 168-171 ◽

Cited By ~ 1

Author(s):

Christiana P. Tai ◽

Taylor Chung ◽

Kishor Avasarala

Keyword(s):

Magnetic Resonance Imaging ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Cardiac Mri ◽

Endomyocardial Fibrosis ◽

Mural Thrombus ◽

Resonance Imaging ◽

Myocardial Biopsy ◽

Non Invasive ◽

The Tropics

AbstractWe present the case of a 4-year-old girl with idiopathic hypereosinophilia syndrome, endomyocardial fibrosis, and mural thrombus. This condition is rarely seen in children outside the tropics. Myocardial biopsy is historically the standard for diagnosis. Reports in adult literature, however, have shown the utility of cardiac MRI as a non-invasive tool for diagnosis, prognosis, and monitoring. To our knowledge, this is the first reported case with serial cardiac MRI in a child.

Download Full-text

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

BMC Medical Imaging ◽

10.1186/s12880-021-00551-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Markus J. Ankenbrand ◽

Liliia Shainberg ◽

Michael Hock ◽

David Lohr ◽

Laura M. Schreiber

Keyword(s):

Neural Networks ◽

Sensitivity Analysis ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Cardiac Mri ◽

Magnetic Resonance Images ◽

Model Interpretation ◽

First Case ◽

Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

Download Full-text

Cardiac Magnetic Resonance in Takotsubo Syndrome

European Cardiology Review ◽

10.15420/ecr.2017:7:2 ◽

2017 ◽

Vol 12 (1) ◽

pp. 58 ◽

Cited By ~ 6

Author(s):

Konstantinos Bratis ◽

Keyword(s):

Heart Failure ◽

Differential Diagnosis ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Risk Stratification ◽

Takotsubo Syndrome ◽

Unknown Aetiology ◽

Non Invasive ◽

Stress Cardiac Magnetic Resonance

Takotsubo syndrome is an acute, profound but reversible heart failure syndrome of unknown aetiology, usually but not always triggered by physical or emotional stress. Cardiac magnetic resonance has become an important tool for the non-invasive assessment of the syndrome, allowing for a comprehensive, safe and reproducible assessment of functional and anatomical myocardial properties, including perfusion, oedema and necrosis. This review focuses on the emerging role of cardiac magnetic resonance for the characterisation, differential diagnosis as well as risk stratification of patients with Takotsubo syndrome.

Download Full-text