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)

Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

IntroductionMagnetic Resonance Imaging (MRI) is a promising alternative to standard x-ray fluoroscopy for the guidance of cardiac catheterization procedures as it enables soft tissue visualization, avoids ionizing radiation and provides improved hemodynamic data. MRI-guided cardiac catheterization procedures currently require frequent manual tracking of the imaging plane during navigation to follow the tip of a gadolinium-filled balloon wedge catheter, which unnecessarily prolongs and complicates the procedures. Therefore, real-time automatic image-based detection of the catheter balloon has the potential to improve catheter visualization and navigation through automatic slice tracking.MethodsIn this study, an automatic, parameter-free, deep-learning-based post-processing pipeline was developed for real-time detection of the catheter balloon. A U-Net architecture with a ResNet-34 encoder was trained on semi-artificial images for the segmentation of the catheter balloon. Post-processing steps were implemented to guarantee a unique estimate of the catheter tip coordinates. This approach was evaluated retrospectively in 7 patients (6M and 1F, age = 7 ± 5 year) who underwent an MRI-guided right heart catheterization procedure with all images acquired in an orientation unseen during training.ResultsThe overall accuracy, specificity and sensitivity of the proposed catheter tracking strategy over all 7 patients were 98.4 ± 2.0%, 99.9 ± 0.2% and 95.4 ± 5.5%, respectively. The computation time of the deep-learning-based segmentation step was ∼10 ms/image, indicating its compatibility with real-time constraints.ConclusionDeep-learning-based catheter balloon tracking is feasible, accurate, parameter-free, and compatible with real-time conditions. Online integration of the technique and its evaluation in a larger patient cohort are now warranted to determine its benefit during MRI-guided cardiac catheterization

Visualization of coronary arteries in paediatric patients using whole-heart coronary magnetic resonance angiography: comparison of image-navigation and the standard approach for respiratory motion compensation

Aims: To investigate the use of respiratory motion compensation using image-based navigation (iNAV) with constant respiratory efficiency using single end-expiratory thresholding (CRUISE) for coronary magnetic resonance angiography (CMRA), and compare it to the conventional diaphragmatic navigator (dNAV) in paediatric patients with congenital or suspected heart disease. Methods: iNAV allowed direct tracking of the respiratory heart motion and was generated using balanced steady state free precession startup echoes. Respiratory gating was achieved using CRUISE with a fixed 50% efficiency. Whole-heart CMRA was acquired with 1.3mm isotropic resolution. For comparison, CMRA with identical imaging parameters were acquired using dNAV. Scan time, visualization of coronary artery origins and mid-course, imaging quality and sharpness was compared between the two sequences. Results: Forty patients (13 females; median weight: 44 kg; median age: 12.6, range: 3 months–17 years) were enrolled. 25 scans were performed in awake patients. A contrast agent was used in 22 patients. The scan time was significantly reduced using iNAV for awake patients (iNAV 7:48 ± 1:26 vs dNAV 9:48 ± 3:11, P = 0.01) but not for patients under general anaesthesia (iNAV = 6:55 ± 1:50 versus dNAV = 6:32 ± 2:16; P = 0.32). In 98% of the cases, iNAV image quality had an equal or higher score than dNAV. The visual score analysis showed a clear difference, favouring iNAV (P = 0.002). The right coronary artery and the left anterior descending vessel sharpness was significantly improved (iNAV: 56.8% ± 10.1% vs dNAV: 53.7% ± 9.9%, P < 0.002 and iNAV: 55.8% ± 8.6% vs dNAV: 53% ± 9.2%, P = 0.001, respectively). Conclusion: iNAV allows for a higher success-rate and clearer depiction of the mid-course of coronary arteries in paediatric patients. Its acquisition time is shorter in awake patients and image quality score is equal or superior to the conventional method in most cases.Medical Engineering at King’s College London WT 088641/Z/09/ZBHF Centre of Excellence RE/08/0

Edge-Enhancement DenseNet for X-ray Fluoroscopy Image Denoising in Cardiac Electrophysiology Procedures

PURPOSE: Reducing X‐ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low‐dose X‐ray fluoroscopy images but may compromise clinically important details required by cardiologists. METHODS: In order to obtain denoised X‐ray fluoroscopy images whilst preserving details, we propose a novel deep‐learning‐based denoising framework, namely edge‐enhancement densenet (EEDN), in which an attention‐awareness edge‐enhancement module is designed to increase edge sharpness. In this framework, a CNN‐based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra‐dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X‐ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low‐dose X‐ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre‐processing tool. RESULTS: The average signal‐to‐noise ratio of X‐ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images. CONCLUSION: The proposed deep learning‐based framework shows promising capability for denoising interventional X‐ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre‐processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory