Model-based indices of early-stage cardiovascular failure and its therapeutic management in Fontan patients

International audienceInvestigating the causes of failure of Fontan circulation in individual patients remains challenging despite detailed combined inva-sive cardiac catheterisation and magnetic resonance (XMR) exams at rest and during stress. In this work, we use a biomechanical model of the heart and Fontan circulation with the components of systemic and pulmonary beds to augment the diagnostic assessment of the patients undergoing the XMR stress exam. We apply our model in 3 Fontan patients and one biventricular "control" case. In all subjects, we obtained important biophysical factors of cardiovascular physiology-contractil-ity, contractile reserve and changes in systemic and pulmonary vascular resistance-which contribute to explaining the mechanism of failure in individual patients. Finally, we used the patient-specific model of one Fontan patient to investigate the impact of changes in pulmonary vas-cular resistance, aiming at in silico testing of pulmonary vasodilation treatments

MRI for Guided Right and Left HeartCardiac Catheterization: A ProspectiveStudy in Congenital Heart Disease

Background: Improvements in outcomes for patients with congenital heart disease (CHD) have increased the need for diagnostic and interventional procedures. Cumulative radiation risk is a growing concern. MRI-guided interventions are a promising ionizing radiation-free, alternative approach. Purpose: To assess the feasibility of MRI-guided catheterization in young patients with CHD using advanced visualization passive tracking techniques. Study Type: Prospective. Population: A total of 30 patients with CHD referred for MRI-guided catheterization and pulmonary vascular resistance analysis (median age/weight: 4 years / 15 kg). Field Strength/Sequence: 1.5T; partially saturated (pSAT) real-time single-shot balanced steady-state free-precession (bSSFP) sequence. Assessment: Images were visualized by a single viewer on the scanner console (interactive mode) or using a commercially available advanced visualization platform (iSuite, Philips). Image quality for anatomy and catheter visualization was evalu ated by three cardiologists with >5 years’ experience in MRI-catheterization using a 1–5 scale (1, poor, 5, excellent). Cathe ter balloon signal-to-noise ratio (SNR), blood and myocardium SNR, catheter balloon/blood contrast-to-noise ratio (CNR), balloon/myocardium CNR, and blood/myocardium CNR were measured. Procedure findings, feasibility, and adverse events were recorded. A fraction of time in which the catheter was visible was compared between iSuite and the interactive mode. Statistical Tests: T-test for numerical variables. Wilcoxon signed rank test for categorical variables. Results: Nine patients had right heart catheterization, 11 had both left and right heart catheterization, and 10 had single ventricle circulation. Nine patients underwent solely MRI-guided catheterization. The mean score for anatomical visualiza tion and contrast between balloon tip and soft tissue was 3.9 0.9 and 4.5 0.7, respectively. iSuite provided a signifi cant improvement in the time during which the balloon was visible in relation to interactive imaging mode (66 17% vs. 46 14%, P < 0.05)

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support setting, AI classification models should ideally avoid confident wrong predictions and maximise the confidence of correct predictions. Models that do this are said to be well-calibrated with regard to confidence. However, relatively little attention has been paid to how to improve calibration when training these models, i.e., to make the training strategy uncertainty-aware. In this work we evaluate three novel uncertainty-aware training strategies comparing against two state-of-the-art approaches. We analyse performance on two different clinical applications: cardiac resynchronisation therapy (CRT) response prediction and coronary artery disease (CAD) diagnosis from cardiac magnetic resonance (CMR) images. The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions. The method reduced the ECE by 17% for CRT response prediction and by 22% for CAD diagnosis when compared to a baseline classifier in which no uncertainty-aware strategy was included. In both applications, as well as reducing the ECE there was a slight increase in accuracy from 69% to 70% and 70% to 72% for CRT response prediction and CAD diagnosis respectively. However, our analysis showed a lack of consistency in terms of optimal models when using different calibration measures. This indicates the need for careful consideration of performance metrics when training and selecting models for complex high-risk applications in healthcare

Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction

Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these models to explain their decisions is important for fostering clinical trust and facilitating clinical translation. Furthermore, for many problems in medicine there is a wealth of existing clinical knowledge to draw upon, which may be useful in generating explanations, but it is not obvious how this knowledge can be encoded into DL models - most models are learnt either from scratch or using transfer learning from a different domain. In this paper we address both of these issues. We propose a novel DL framework for image-based classification based on a variational autoencoder (VAE). The framework allows prediction of the output of interest from the latent space of the autoencoder, as well as visualisation (in the image domain) of the effects of crossing the decision boundary, thus enhancing the interpretability of the classifier. Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge. The framework can predict outputs as well as explanations for these outputs, and also raises the possibility of discovering new biomarkers that are separate (or disentangled) from the existing knowledge. We demonstrate our framework on the problem of predicting response of patients with cardiomyopathy to cardiac resynchronization therapy (CRT) from cine cardiac magnetic resonance images. The sensitivity and specificity of the proposed model on the task of CRT response prediction are 88.43% and 84.39% respectively, and we showcase the potential of our model in enhancing understanding of the factors contributing to CRT response.Comment: MICCAI 2020 conferenc

MRI for Guided Right and Left Heart Cardiac Catheterization: A Prospective Study in Congenital Heart Disease

[Background] Improvements in outcomes for patients with congenital heart disease (CHD) have increased the need for diagnostic and interventional procedures. Cumulative radiation risk is a growing concern. MRI-guided interventions are a promising ionizing radiation-free, alternative approach.[Purpose] To assess the feasibility of MRI-guided catheterization in young patients with CHD using advanced visualization passive tracking techniques.[Study Type] Prospective.[Population] A total of 30 patients with CHD referred for MRI-guided catheterization and pulmonary vascular resistance analysis (median age/weight: 4 years / 15 kg).[Field Strength/Sequence] 1.5T; partially saturated (pSAT) real-time single-shot balanced steady-state free-precession (bSSFP) sequence.[Assessment] Images were visualized by a single viewer on the scanner console (interactive mode) or using a commercially available advanced visualization platform (iSuite, Philips). Image quality for anatomy and catheter visualization was evaluated by three cardiologists with >5 years' experience in MRI-catheterization using a 1–5 scale (1, poor, 5, excellent). Catheter balloon signal-to-noise ratio (SNR), blood and myocardium SNR, catheter balloon/blood contrast-to-noise ratio (CNR), balloon/myocardium CNR, and blood/myocardium CNR were measured. Procedure findings, feasibility, and adverse events were recorded. A fraction of time in which the catheter was visible was compared between iSuite and the interactive mode.[Statistical Tests] T-test for numerical variables. Wilcoxon signed rank test for categorical variables.[Results] Nine patients had right heart catheterization, 11 had both left and right heart catheterization, and 10 had single ventricle circulation. Nine patients underwent solely MRI-guided catheterization. The mean score for anatomical visualization and contrast between balloon tip and soft tissue was 3.9 ± 0.9 and 4.5 ± 0.7, respectively. iSuite provided a significant improvement in the time during which the balloon was visible in relation to interactive imaging mode (66 ± 17% vs. 46 ± 14%, P < 0.05).[Data Conclusion] MRI-guided catheterizations were carried out safely and is feasible in children and adults with CHD. The pSAT sequence offered robust and simultaneous high contrast visualization of the catheter and cardiac anatomy.Peer reviewe