Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.

HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination.

PURPOSE: Interactive cardiac MRI is used for fast scan planning and MR-guided interventions. However, the requirement for real-time acquisition and near-real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression). METHODS: A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework-HyperSLICE- was compared with conventional Cartesian real-time and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post hoc Nemenyi test (p < 0.05). RESULTS: In simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging. CONCLUSION: HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories.

2D sodium MRI of the human calf using half-sinc excitation pulses and compressed sensing.

PURPOSE: Sodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in-plane resolution but typically has long TEs. Half-sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half-sinc acquisitions while maintaining SNR and TSC. METHODS: Ex vivo and in vivo TSC were compared between 2D spiral sequences with full-sinc (TE = 0.73 ms, scan time ≈ 5 min) and half-sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half-sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images. RESULTS: TSC was significantly higher from half-sinc than from full-sinc acquisitions, ex vivo and in vivo. Ex vivo, half-sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half-sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs. CONCLUSION: 2D sodium MRI with half-sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins.

Native myocardial T1 and right ventricular size by CMR predict outcome in systemic sclerosis-associated pulmonary hypertension.

OBJECTIVES: Measures of right heart size and function are prognostic in systemic sclerosis-associated pulmonary hypertension (SSc-PH), but the importance of myocardial tissue characterisation remains unclear. We aimed to investigate the predictive potential and interaction of cardiovascular magnetic resonance (CMR) myocardial tissue characterisation and right heart size and function in SSc-PH. METHODS: A retrospective, single-centre, observational study of 148 SSc-PH patients confirmed by right heart catheterization who underwent clinically indicated CMR including native myocardial T1 and T2 mapping from 2016 to 2023 was performed. RESULTS: Sixty-six (45%) patients died during follow-up (median 3.5 years, range 0.1-7.3). Patients who died were older (65 vs 60 years, P = 0.035) with more dilated (P < 0.001), hypertrophied (P = 0.013) and impaired (P < 0.001) right ventricles, more dilated right atria (P = 0.043) and higher native myocardial T1 (P < 0.001).After adjustment for age, indexed right ventricular end-systolic volume (RVESVi, P = 0.0023) and native T1 (P = 0.0024) were independent predictors of all-cause mortality. Both RVESVi and native T1 remained independently predictive after adjusting for age and PH subtype (RVESVi P < 0.001, T1 P = 0.0056). Optimal prognostic thresholds for RVESVi and native T1 were ≤38 mL/m2 and ≤1119 ms, respectively (P < 0.001). Patients with RVESVi ≤ 38 mL/m2 and native T1 ≤ 1119 ms had significantly better outcomes than all other combinations (P < 0.001). Furthermore, patients with RVESVi > 38mL/m2 and native T1 ≤ 1119 ms had significantly better survival than patients with RVESVi > 38mL/m2 and native T1 > 1119ms (P = 0.017). CONCLUSION: We identified prognostically relevant CMR metrics and thresholds for patients with SSc-PH. Assessing myocardial tissue characterisation alongside right ventricular function confers added value in SSc-PH and may represent an additional treatment target.

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.

Characterization and Z-score Calculation of Cardiac MRI parameters in patients after the Fontan operation. A Fontan Outcome Registry using CMR Examinations (FORCE) study.

BACKGROUND: Cardiac magnetic resonance (CMR) offers valuable hemodynamic insights post-Fontan, but is limited by the absence of normative single ventricle data. The Fontan Outcomes Registry using CMR Examinations (FORCE) is a large international Fontan-specific CMR registry. This study used FORCE registry data to evaluate expected CMR ventricular size/function and create Fontan-specific z-scores adjusting for ventricular morphology (VM) in healthier Fontan patients. METHODS: "Healthier" Fontan patients were defined as patients free of adverse outcomes, who are New York Heart Association Class I, have mild or less valve disease, and <30% aortopulmonary collateral burden. General linear modeling was performed on 70% of the dataset to create z-scores for volumes and function. Models were tested using the remainder (30%) of the data. The z-scores were compared between children and adults. The z-scores were also compared between "healthier" Fontan and patients with adverse outcomes (death, listing for transplantation or multiorgan disease). RESULTS: The "healthier" Fontan population included 885 patients (15.0 ± 7.6 years) from 18 institutions with 1,156 CMR examinations. Patients with left ventricle morphology had lower volume, mass and higher ejection fraction (EF) compared to right or mixed (two-ventricles) morphology (p<0.001 for all pairwise comparisons). Gender, BSA and VM were used in z-scores. Of the "healthier" Fontan patients, 647 were children <18 years and 238 were adults. Adults had lower ascending aorta flow (2.9 ± 0.7 vs 3.3 ± 0.8L/min/m2, p<0.001) and ascending aorta flow z-scores (-0.16 ± 1.23 vs 0.05 ± 0.95, 0.02) compared to children. Additionally, there were 1595 patients with adverse outcomes who were older (16.1 ± 9.3 vs 15.0 ± 7.6, p<0.001) and less likely to have LV morphology (35 vs 47%, p<0.001). Patients with adverse outcomes had higher z-scores for ventricular volume and mass and lower z-scores for EF and ascending aorta flow compared to the "healthier" Fontan cohort. CONCLUSION: This is the first study to generate CMR z-scores post-Fontan. Importantly the z-scores were generated and tested in "healthier" Fontan patients and both pediatric and adult Fontan patients. These equations may improve CMR-based risk stratification after the Fontan operation.

Design and implementation of multicenter pediatric and congenital studies with cardiovascular magnetic resonance: Big data in smaller bodies.

Cardiovascular magnetic resonance (CMR) has become the reference standard for quantitative and qualitative assessment of ventricular function, blood flow, and myocardial tissue characterization. There is a preponderance of large CMR studies and registries in adults; However, similarly powered studies are lacking for the pediatric and congenital heart disease (PCHD) population. To date, most CMR studies in children are limited to small single or multicenter studies, thereby limiting the conclusions that can be drawn. Within the PCHD CMR community, a collaborative effort has been successfully employed to recognize knowledge gaps with the aim to embolden the development and initiation of high-quality, large-scale multicenter research. In this publication, we highlight the underlying challenges and provide a practical guide toward the development of larger, multicenter initiatives focusing on PCHD populations, which can serve as a model for future multicenter efforts.

The beating heart: artificial intelligence for cardiovascular application in the clinic.

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

Predicting Peri-Operative Cardiorespiratory Adverse Events in Children with Idiopathic Pulmonary Arterial Hypertension Undergoing Cardiac Catheterization Using Echocardiography: A Cohort Study.

General anesthesia in children with idiopathic pulmonary arterial hypertension (PAH) carries an increased risk of peri-operative cardiorespiratory complications though risk stratifying individual children pre-operatively remains difficult. We report the incidence and echocardiographic risk factors for adverse events in children with PAH undergoing general anesthesia for cardiac catheterization. Echocardiographic, hemodynamic, and adverse event data from consecutive PAH patients are reported. A multivariable predictive model was developed from echocardiographic variables identified by Bayesian univariable logistic regression. Model performance was reported by area under the curve for receiver operating characteristics (AUCroc) and precision/recall (AUCpr) and a pre-operative scoring system derived (0-100). Ninety-three children underwent 158 cardiac catheterizations with mean age 8.8 ± 4.6 years. Adverse events (n = 42) occurred in 15 patients (16%) during 16 catheterizations (10%) including cardiopulmonary resuscitation (n = 5, 3%), electrocardiographic changes (n = 3, 2%), significant hypotension (n = 2, 1%), stridor (n = 1, 1%), and death (n = 2, 1%). A multivariable model (age, right ventricular dysfunction, and dilatation, pulmonary and tricuspid regurgitation severity, and maximal velocity) was highly predictive of adverse events (AUCroc 0.86, 95% CI 0.75 to 1.00; AUCpr 0.68, 95% CI 0.50 to 0.91; baseline AUCpr 0.10). Pre-operative risk scores were higher in those who had a subsequent adverse event (median 47, IQR 43 to 53) than in those who did not (median 23, IQR 15 to 33). Pre-operative echocardiography informs the risk of peri-operative adverse events and may therefore be useful both for consent and multi-disciplinary care planning.

Predictors of outcomes in mild pulmonary hypertension according to 2022 ESC/ERS Guidelines: the EVIDENCE-PAH UK study.

BACKGROUND AND AIMS: Interventional studies in pulmonary arterial hypertension completed to date have shown to be effective in symptomatic patients with significantly elevated mean pulmonary artery pressure (mPAP) (≥25 mmHg) and pulmonary vascular resistance (PVR) > 3 Wood Unit (WU). However, in health the mPAP does not exceed 20 mmHg and PVR is 2 WU or lower, at rest. The ESC/ERS guidelines have recently been updated to reflect this. There is limited published data on the nature of these newly defined populations (mPAP 21-24 mmHg and PVR >2-≤3 WU) and the role of comorbidity in determining their natural history. With the change in guidelines, there is a need to understand this population and the impact of the ESC/ERS guidelines in greater detail. METHODS: A retrospective nationwide evaluation of the role of pulmonary haemodynamics and comorbidity in predicting survival among patients referred to the UK pulmonary hypertension (PH) centres between 2009 and 2017. In total, 2929 patients were included in the study. Patients were stratified by mPAP (<21 mmHg, 21-24 mmHg, and ≥25 mmHg) and PVR (≤2 WU, > 2-≤3 WU, and >3 WU), with 968 (33.0%) in the mPAP <21 mmHg group, 689 (23.5%) in the mPAP 21-24 mmHg group, and 1272 (43.4%) in the mPAP ≥25 mmHg group. RESULTS: Survival was negatively correlated with mPAP and PVR in the population as a whole. Survival in patients with mildly elevated mPAP (21-24 mmHg) or PVR (>2-≤3WU) was lower than among those with normal pressures (mPAP <21 mmHg) and normal PVR (PVR ≤ 2WU) independent of comorbid lung and heart disease [hazard ratio (HR) 1.36, 95% confidence interval (CI) 1.14-1.61, P = .0004 for mPAP vs. HR 1.28, 95% CI 1.10-1.49, P = .0012 for PVR]. Among patients with mildly elevated mPAP, a mildly elevated PVR remained an independent predictor of survival when adjusted for comorbid lung and heart disease (HR 1.33, 95% CI 1.01-1.75, P = .042 vs. HR 1.4, 95% CI 1.06-1.86, P = .019). 68.2% of patients with a mPAP 21-24 mmHg had evidence of underlying heart or lung disease. Patients with mildly abnormal haemodynamics were not more symptomatic than patients with normal haemodynamics. Excluding patients with heart and lung disease, connective tissue disease was associated with a poorer survival among those with PH. In this subpopulation evaluating those with a mPAP of 21-24 mmHg, survival curves only diverged after 5 years. CONCLUSIONS: This study supports the change in diagnostic category of the ESC/ERS guidelines in a PH population. The newly included patients have an increased mortality independent of significant lung or heart disease. The majority of patients in this new category have underlying heart or lung disease rather than an isolated pulmonary vasculopathy. Mortality is higher if comorbidity is present. Rigorous phenotyping will be pivotal to determine which patients are at risk of progressive vasculopathic disease and in whom surveillance and recruitment to studies may be of benefit. This study provides an insight into the population defined by the new guidelines.

Rapid desensitization through immunoadsorption during cardiopulmonary bypass. A novel method to facilitate human leukocyte antigen incompatible heart transplantation.

BACKGROUND: Anti-human leukocyte antigen (HLA)-antibody production represents a major barrier to heart transplantation, limiting recipient compatibility with potential donors and increasing the risk of complications with poor waiting-list outcomes. Currently there is no consensus to when desensitization should take place, and through what mechanism, meaning that sensitized patients must wait for a compatible donor for many months, if not years. We aimed to determine if intraoperative immunoadsorption could provide a potential desensitization methodology. METHODS: Anti-HLA antibody-containing whole blood was added to a Cardiopulmonary bypass (CPB) circuit set up to mimic a 20 kg patient undergoing heart transplantation. Plasma was separated and diverted to a standalone, secondary immunoadsorption system, with antibody-depleted plasma returned to the CPB circuit. Samples for anti-HLA antibody definition were taken at baseline, when combined with the CPB prime (on bypass), and then every 20 min for the duration of treatment (total 180 min). RESULTS: A reduction in individual allele median fluorescence intensity (MFI) to below clinically relevant levels (<1000 MFI), and in the majority of cases below the lower positive detection limit (<500 MFI), even in alleles with a baseline MFI >4000 was demonstrated. Reduction occurred in all cases within 120 min, demonstrating efficacy in a time period usual for heart transplantation. Flowcytometric crossmatching of suitable pseudo-donor lymphocytes demonstrated a change from T cell and B cell positive channel shifts to negative, demonstrating a reduction in binding capacity. CONCLUSIONS: Intraoperative immunoadsorption in an ex vivo setting demonstrates clinically relevant reductions in anti-HLA antibodies within the normal timeframe for heart transplantation. This method represents a potential desensitization technique that could enable sensitized children to accept a donor organ earlier, even in the presence of donor-specific anti-HLA antibodies.

Twenty-Year Experience and Outcomes in a National Pediatric Pulmonary Hypertension Service.

Rationale: Pediatric pulmonary hypertension is an important cause of childhood morbidity and mortality, but there are limited data on the range of associated diseases, contributions of different pulmonary hypertension subtypes, therapeutic strategies, and clinical outcomes in children. Objectives: To report the 20-year experience of a large UK National Pediatric Pulmonary Hypertension Service focusing on epidemiology and clinical outcomes. Methods: Consecutive patients presenting between 2001 and 2021 were included, and survival analysis was performed for incident patients. Measurements and Main Results: Of 1,353 patients assessed, a pulmonary hypertension diagnosis was made in 1,101 (81.4%) patients (51% female, median age, 2.6 [interquartile range, 0.8-8.2] years). The most common form was pulmonary arterial hypertension in 48%, followed by 32.3% with pulmonary hypertension due to lung disease. Multiple contributory causes of pulmonary hypertension were common, with 16.9% displaying features of more than one diagnostic group. The annual incidence of childhood pulmonary hypertension was 3.5 (95% confidence interval [CI], 3.3-3.8) per 1 million children, and the prevalence was 18.1 (95% CI, 15.8-20.4) per 1 million. The incidence was highest for pulmonary hypertension due to lung disease in infancy (15.0 [95% CI, 12.7-17.2] per 1 million per year). Overall, 82.4% patients received pulmonary arterial hypertension therapy, and escalation to triple therapy during follow-up was required in 13.1%. In 970 (88.1%) incident patients, transplant-free survival was 86.7% (95% CI, 84.5-89%) at 1 and 68.6% (95% CI, 64.7-72.6%) at 10 years. Pulmonary hypertension due to left heart disease had the lowest survival (hazard ratio, 2.0; 95% CI, 1.36-2.94; P < 0.001). Conclusions: Clinical phenotypes of pediatric pulmonary hypertension are heterogeneous and overlapping, with clinical phenotypes that evolve throughout childhood. Despite widespread use of pulmonary arterial hypertension therapy, the prognosis remains poor.

FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation.

PURPOSE: Real-time monitoring of cardiac output (CO) requires low-latency reconstruction and segmentation of real-time phase-contrast MR, which has previously been difficult to perform. Here we propose a deep learning framework for "FReSCO" (Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring). METHODS: Deep artifact suppression and segmentation U-Nets were independently trained. Breath-hold spiral phase-contrast MR data (N = 516) were synthetically undersampled using a variable-density spiral sampling pattern and gridded to create aliased data for training of the artifact suppression U-net. A subset of the data (N = 96) was segmented and used to train the segmentation U-net. Real-time spiral phase-contrast MR was prospectively acquired and then reconstructed and segmented using the trained models (FReSCO) at low latency at the scanner in 10 healthy subjects during rest, exercise, and recovery periods. Cardiac output obtained via FReSCO was compared with a reference rest CO and rest and exercise compressed-sensing CO. RESULTS: The FReSCO framework was demonstrated prospectively at the scanner. Beat-to-beat heartrate, stroke volume, and CO could be visualized with a mean latency of 622 ms. No significant differences were noted when compared with reference at rest (bias = -0.21 ± 0.50 L/min, p = 0.246) or compressed sensing at peak exercise (bias = 0.12 ± 0.48 L/min, p = 0.458). CONCLUSIONS: The FReSCO framework was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.

Automatic segmentation of the great arteries for computational hemodynamic assessment.

BACKGROUND: Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies. METHODS: 90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors. RESULTS: The network's Dice score (ML vs GT) was 0.945 (interquartile range: 0.929-0.955) for the aorta and 0.885 (0.851-0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5-15.7%) and 4.1% (3.1-6.9%), respectively, and for the pulmonary arteries 14.6% (11.5-23.2%) and 6.3% (4.3-7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2). CONCLUSIONS: ML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.

Society for Cardiovascular Magnetic Resonance/European Society of Cardiovascular Imaging/American Society of Echocardiography/Society for Pediatric Radiology/North American Society for Cardiovascular Imaging Guidelines for the use of cardiovascular magnetic resonance in pediatric congenital and acquired heart disease : Endorsed by The American Heart Association.

Cardiovascular magnetic resonance (CMR) has been utilized in the management and care of pediatric patients for nearly 40 years. It has evolved to become an invaluable tool in the assessment of the littlest of hearts for diagnosis, pre-interventional management and follow-up care. Although mentioned in a number of consensus and guidelines documents, an up-to-date, large, stand-alone guidance work for the use of CMR in pediatric congenital 36 and acquired 35 heart disease endorsed by numerous Societies involved in the care of these children is lacking. This guidelines document outlines the use of CMR in this patient population for a significant number of heart lesions in this age group and although admittedly, is not an exhaustive treatment, it does deal with an expansive list of many common clinical issues encountered in daily practice.

Real-time deep artifact suppression using recurrent U-Nets for low-latency cardiac MRI.

PURPOSE: Real-time low latency MRI is performed to guide various cardiac interventions. Real-time acquisitions often require iterative image reconstruction strategies, which lead to long reconstruction times. In this study, we aim to reconstruct highly undersampled radial real-time data with low latency using deep learning. METHODS: A 2D U-Net with convolutional long short-term memory layers is proposed to exploit spatial and preceding temporal information to reconstruct highly accelerated tiny golden radial data with low latency. The network was trained using a dataset of breath-hold CINE data (including 770 time series from 7 different orientations). Synthetic paired data were created by retrospectively undersampling the magnitude images, and the network was trained to recover the target images. In the spirit of interventional imaging, the network was trained and tested for varying acceleration rates and orientations. Data were prospectively acquired and reconstructed in real time in 1 healthy subject interactively and in 3 patients who underwent catheterization. Images were visually compared to sliding window and compressed sensing reconstructions and a conventional Cartesian real-time sequence. RESULTS: The proposed network generalized well to different acceleration rates and unseen orientations for all considered metrics in simulated data (less than 4% reduction in structural similarity index compared to similar acceleration and orientation-specific networks). The proposed reconstruction was demonstrated interactively, successfully depicting catheters in vivo with low latency (39 ms, including 19 ms for deep artifact suppression) and an image quality comparing favorably to other reconstructions. CONCLUSION: Deep artifact suppression was successfully demonstrated in the time-critical application of non-Cartesian real-time interventional cardiac MR.

Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning.

BACKGROUND: Contrast-enhanced magnetic resonance angiography (MRA) is used to assess various cardiovascular conditions. However, gadolinium-based contrast agents (GBCAs) carry a risk of dose-related adverse effects. PURPOSE: To develop a deep learning method to reduce GBCA dose by 80%. STUDY TYPE: Retrospective and prospective. POPULATION: A total of 1157 retrospective and 40 prospective congenital heart disease patients for training/validation and testing, respectively. FIELD STRENGTH/SEQUENCE: A 1.5 T, T1-weighted three-dimensional (3D) gradient echo. ASSESSMENT: A neural network was trained to enhance low-dose (LD) 3D MRA using retrospective synthetic data and tested with prospective LD data. Image quality for LD (LD-MRA), enhanced LD (ELD-MRA), and high-dose (HD-MRA) was assessed in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and a quantitative measure of edge sharpness and scored for perceptual sharpness and contrast on a 1-5 scale. Diagnostic confidence was assessed on a 1-3 scale. LD- and ELD-MRA were assessed against HD-MRA for sensitivity/specificity and agreement of vessel diameter measurements (aorta and pulmonary arteries). STATISTICAL TESTS: SNR, CNR, edge sharpness, and vessel diameters were compared between LD-, ELD-, and HD-MRA using one-way repeated measures analysis of variance with post-hoc t-tests. Perceptual quality and diagnostic confidence were compared using Friedman's test with post-hoc Wilcoxon signed-rank tests. Sensitivity/specificity was compared using McNemar's test. Agreement of vessel diameters was assessed using Bland-Altman analysis. RESULTS: SNR, CNR, edge sharpness, perceptual sharpness, and perceptual contrast were lower (P < 0.05) for LD-MRA compared to ELD-MRA and HD-MRA. SNR, CNR, edge sharpness, and perceptual contrast were comparable between ELD and HD-MRA, but perceptual sharpness was significantly lower. Sensitivity/specificity was 0.824/0.921 for LD-MRA and 0.882/0.960 for ELD-MRA. Diagnostic confidence was 2.72, 2.85, and 2.92 for LD, ELD, and HD-MRA, respectively (PLD-ELD , PLD-HD  < 0.05). Vessel diameter measurements were comparable, with biases of 0.238 (LD-MRA) and 0.278 mm (ELD-MRA). DATA CONCLUSION: Deep learning can improve contrast in LD cardiovascular MRA. LEVEL OF EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Reduced exercise capacity in patients with systemic sclerosis is associated with lower peak tissue oxygen extraction: a cardiovascular magnetic resonance-augmented cardiopulmonary exercise study.

BACKGROUND: Exercise intolerance in systemic sclerosis (SSc) is typically attributed to cardiopulmonary limitations. However, problems with skeletal muscle oxygen extraction have not been fully investigated. This study used cardiovascular magnetic resonance (CMR)-augmented cardiopulmonary exercise testing (CMR-CPET) to simultaneously measure oxygen consumption and cardiac output. This allowed calculation of arteriovenous oxygen content gradient, a recognized marker of oxygen extraction. We performed CMR-CPET in 4 groups: systemic sclerosis (SSc); systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH); non-connective tissue disease pulmonary hypertension (NC-PAH); and healthy controls. METHODS: We performed CMR-CPET in 60 subjects (15 in each group) using a supine ergometer following a ramped exercise protocol until exhaustion. Values for oxygen consumption, cardiac output and oxygen content gradient, as well as ventricular volumes, were obtained at rest and peak-exercise for all subjects. In addition, T1 and T2 maps were acquired at rest, and the most recent clinical measures (hemoglobin, lung function, 6-min walk, cardiac and catheterization) were collected. RESULTS: All patient groups had reduced peak oxygen consumption compared to healthy controls (p < 0.022). The SSc and SSc-PAH groups had reduced peak oxygen content gradient compared to healthy controls (p < 0.03). Conversely, the SSc-PAH and NC-PH patients had reduced peak cardiac output compared to healthy controls and SSc patients (p < 0.006). Higher hemoglobin was associated with higher peak oxygen content gradient (p = 0.025) and higher myocardial T1 was associated with lower peak stroke volume (p = 0.011). CONCLUSIONS: Reduced peak oxygen consumption in SSc patients is predominantly driven by reduced oxygen content gradient and in SSc-PAH patients this was amplified by reduced peak cardiac output. Trial registration The study is registered with ClinicalTrials.gov Protocol Registration and Results System (ClinicalTrials.gov ID: 100358).

The role of artificial intelligence in healthcare: a structured literature review.

BACKGROUND/INTRODUCTION: Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. METHODS: The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. RESULTS: The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. CONCLUSIONS: The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Cardiovascular Magnetic Resonance in Congenital Heart Disease: Focus on Heart Failure.

Over the past decade, cardiovascular magnetic resonance (CMR) has become a mainstream noninvasive imaging tool for assessment of adult and pediatric patients with congenital heart disease. It provides comprehensive anatomic and hemodynamic information that echocardiography and catheterization alone do not provide. Extracardiac anatomy can be delineated with high spatial resolution, intracardiac anatomy can be imaged in multiple planes, and functional assessment can be made accurately and with high reproducibility. In patients with heart failure, CMR provides not only reference standard evaluation of ventricular volumes and function but also information about the possible causes of dysfunction.